DEV Community: Dmitry Baraishuk

How to Summarize Huge Documents with LLMs: Beyond Token Limits and Basic Prompts

Dmitry Baraishuk — Tue, 26 Aug 2025 19:17:00 +0000

Summarizing text is one of the main use cases for large language models. Clients often want to summarize articles, financial documents, chat history, tables, pages, books, and more. We all expect that LLM will distill only the important pieces of information, especially from long texts. However, this isn't always possible with the expected level of quality. Even a larger token limit isn’t a guaranteed solution. Fortunately, there are approaches that help summarize texts of different lengths - whether it’s a couple of sentences, paragraphs, pages, an entire book, or an unknown amount of text.

This guide, built from Belitsoft's experience as a custom software development company, explores those advanced approaches. We provide full-cycle generative AI implementation, from selecting model architectures to deploying scalable systems for processing complex documents like legal contracts, medical records, and financial disclosures. In this article, we'll break down the practical techniques that make large-scale LLM summarization work.

Basic Prompts to Summarize a Couple of Sentences

This is default behavior across almost any LLM, whether it's OpenAI, Anthropic, Mistral, Llama or others.

In this case, we simply copy and paste some text from the source and put it inside a prompt, giving the LLM an instruction like: “Please provide a summary of the following passage”.

If the output is still a little too complicated, we can adjust the instructions to get a different type of summary, for example: “Please provide a summary of the following text. Your output should be in a manner that a five-year-old would understand”, to get a much more digestible result.

This approach works when there aren’t too many tokens in the prompt (let’s say 200 tokens or 150 words). But as the number of tokens increases, like with larger documents, the summarization using basic prompts won’t be accurate and many things will be omitted, whether they were important for us or not.

Prompt Templates to Get the Summary in a Preferred Format

Prompt Templates help deal with the issue of inconsistent summary output across different texts - something that often happens when the input is long and you're using only a basic prompt like “Summarize this”.

For example, the prompt template may look like a rule: "Please write a one-sentence summary of the following text {}". Notice that we ask for “one sentence” instead of just a “summarize”.

With Prompt Templates, we can influence the quality of summary output in specific directions: format and length (“1 sentence” or “3 bullet points”), tone (simple language, executive style), and focus (only risks, only outcomes, only decisions). Keeping the output structure uniform is what we need for automation.

MapReduce method to summarize…summaries

Most LLM users think of summarization as "throw the whole text at the model → get a summary”. That’s why the output is often not as expected.

But the MapReduce method changes that into "break the text into chunks → summarize each →summarize the summaries”. You first generate individual summaries (map), then combine and condense them into one final summary (reduce). This reflects how we deal with long texts: we read in parts, take notes, then consolidate them to get a big picture.

So again, the main idea of the MapReduce method is to “chunk our document into pieces (that fit within the token limit), get a summary of each individual chunk, and then finally get a summary of the summaries”.

MapReduce method is mostly used for creating customized apps or workflows that run on top of general-purpose LLMs (like GPT-4, Claude, LLaMA, etc.) using frameworks like LangChain or raw Python.

Here is the general workflow for summarization using the MapReduce technique:

Load the input document into RAM (LangChain equivalent: open(file).read())
Estimate whether the text exceeds the token limit (for example, 2,000 tokens) (LangChain equivalent: llm.get_num_tokens(text))
Split the text into smaller chunks that fit within the LLM's context window (LangChain equivalent: RecursiveCharacterTextSplitter())
Convert chunks into a structured format (for example, a list of texts or document objects) (LangChain equivalent: create_documents())
Write a per-chunk prompt template to summarize each individual chunk (LangChain equivalent: map_prompt_template)
Write a final combine prompt template that summarizes the chunk-level summaries into bullet points or another format (LangChain equivalent: combine_prompt_template) 7.** Run the Map phase** - apply the per-chunk prompt to each chunk (LangChain: map_reduce)
Run the Reduce phase - apply the final combine prompt to the intermediate summaries (LangChain: map_reduce)
Output the final result - the combined summary (in list, bullet point, or paragraph form) (LangChain equivalent: print(output))

As you can see, if you try to build a real application that does reliable document summarization - say, for legal teams, financial analysts, internal knowledge search, or anything beyond casual reading - the tools available in the ChatGPT web interface or a raw API call aren't enough on their own.

Yes, you can upload a PDF into a web version of ChatGPT, and ask it to apply the MapReduce method, and if the file is small and the content is simple, you’ll get a decent summary. But, you’ll hit limitations: unpredictable behavior, like content skipped or compressed too aggressively. It's very hard to control how the content is split, to loop over each section with a consistent prompt, and combine those outputs in a structured way.

Even if you use OpenAI API, you still have to build everything else yourself: chunk the input, manage prompts for each part, send multiple API calls, and then combine the outputs. The API just gives you the LLM - it doesn’t provide a system for managing workflows.

That’s where a middle layer comes in. You build a lightweight backend that handles the logic: read a long document, split it, summarize each piece with the same prompt, and combine the results at the end. This logic is what frameworks like LangChain or LlamaIndex help with, but you can also build it yourself in plain Python.

Embeddings and Clustering to Summarize… Books

Some PDFs may contain as much content as a full book, and sometimes we want to summarize that amount of text as well. What kind of size are we talking about? For example, 140,000 tokens - roughly 100,000+ words.

If you send a prompt with that much text to a commercial LLM, it’ll cost you a significant amount, even if the model can process it in one go. Commercial LLMs like ChatGPT charge you twice: once for the input tokens it processes, and once for the output tokens it generates.

Moreover, there’s something important to understand that should make you think twice before applying the MapReduce method to such a large amount of text: semantic similarity. Experienced readers know that a book rarely contains completely unique content from beginning to end. The same ideas are often repeated - just phrased in different ways.

That’s yet another reason against blindly chunking a book and applying MapReduce - you’ll likely send multiple chunks with the same meaning to the LLM, get nearly identical summaries, and overpay for it. That’s not the kind of situation smart people who care about costs want to end up in.

Starting from the idea of semantic similarity, we may realize that all we need to do before sending a book’s text to an LLM is remove parts that are similar (in other words, not important for the summary because they repeat the same ideas). So, in general, our goal becomes compressing the meaning before submitting it for processing.

This is exactly the stage where we start thinking about using text preprocessing methods like embedding (converting each text chunk into a vector that captures its meaning, like [0.11, -0.44, 0.85, ...], so we can measure similarity between chunks) and clustering (grouping similar vectors together to avoid redundancy and pick one best passage from each group).

So again, we’re not going to feed the entire book to the model - only the important parts, let’s say the 10 best sections that represent most of the meaning. We ignore the rest because it adds no new angle or dimension to what we want to learn from the summary.

At this stage, what we really want is to scientifically select only those sections of the book that represent a holistic and diverse view - covering the most important, distinct parts that describe the book best. To do this, we need to form “meaning clusters,” and from each diverse cluster, we want to select just one best representative - the one that is closest to the “cluster centroid” (each cluster has its own centroid).

Building AI Agents to Summarize Documents

At the very least, what should we do if our workflow logic requires summarizing an unknown amount of text? We should use agents for this.

Such agents are able to handle complex tasks. For example, the question we want to ask the LLM requires several steps to answer: searching more than one source, summarizing, and combining the findings.

The agent should grab the first doc, pull out the key points, then do the same with the second, etc. After that, it should combine overlapping ideas and write the final answer. The agentic approach is designed to handle that chain of steps automatically.

Transitioning to Microsoft Fabric from Power BI Premium: Key Challenges and How to Overcome Them

Dmitry Baraishuk — Tue, 26 Aug 2025 10:33:10 +0000

Microsoft Fabric expands what Power BI Premium already started (dashboards, modeling, ETL, basic AI) by adding the missing pieces (Spark for engineering, real-time pipelines, and full lakehouse architecture) into a full-stack platform. It's official: Power BI Premium per capacity SKUs are being retired. Fabric capacities are the new standard. The move to Fabric isn’t optional anymore.

Switching to Microsoft Fabric isn’t simply upgrading your BI tool — it changes how your entire data ecosystem works. Getting the most out of Fabric requires more than just dashboards. It calls for combining analytics expertise with strong data engineering skills, like SQL, Python, and Spark, and using current advanced techniques for managing huge, rapid data streams. As a custom development firm, Belitsoft helps companies make this transition without disruption. Our team takes the stress out of migration via roadmap planning, architectural redesign, and rollout management, so you can concentrate on your business without concern for downtime or unexpected costs. In this article, we’ll explain why Power BI Premium is being retired and its implication for your analytics strategy.

Technical and Organizational Capabilities Required

To migrate from Power BI Premium to Microsoft Fabric, companies need to build up both the tech skills and the organization’s muscle to handle the shift.

Broad Technical Skill Set

Fabric brings everything under one roof: data integration (Data Factory), engineering (Spark, notebooks), warehousing (Synapse SQL), and classic BI (Power BI). But with that comes a shift in expectations. Knowing Power BI isn’t enough anymore. Your team needs to be fluent in SQL, DAX, Python, Spark, Delta Lake. If they are coming from a dashboards-and-visuals world, this is a whole new ballgame. The learning curve is real, especially for teams without deep data engineering experience.

Data Architecture & Planning

Fabric is a greenfield environment, which means full flexibility, but zero guardrails. No out-of-the-box structure, no default best practices. That’s great if you’ve got strong data architects. If not, it’s a recipe for chaos. Building from scratch means you need to get it right early: workflows, pipelines, modeling. Think long-term from day one. Use of medallion architecture in OneLake is a good example of doing it right. In highly regulated sectors like healthcare and fintech, a BI consultant with domain knowledge can help define early architecture that supports compliance, governance, and long-term scalability from the ground up.

Cross-Functional Collaboration

Fabric brings everyone into the same space: data engineers, BI devs, data scientists. The roles that used to sit apart are now working side by side. That’s why it’s not just a platform shift, it’s a team shift. Companies need to start building cross-disciplinary teams and getting departments to actually collaborate; not just hand stuff off. In some cases, that means spinning up a central DataOps team or a center of excellence to keep things from drifting.

Governance and Data Management

Companies should have or develop capabilities in data governance, security, and compliance that span multiple services. Fabric doesn’t automatically centralize governance across its components, so skills with tools like Microsoft Purview for metadata management and lineage can help fill this gap. Role-based access controls, workspace management, and policies need to be enforced consistently across the unified environment.

DevOps and Capacity Management

Fabric isn’t set-it-and-forget-it. It runs on Azure capacities, and depending on how you set it up, you might be dealing with a pay-as-you-go model instead of fixed capacity. That means teams need to know how to monitor and tune resource usage: things like how capacity units get eaten up, when to scale, and how to schedule workloads so you are not burning money during off-hours. Without that visibility, performance takes a hit or costs spiral. A FinOps mindset helps here. Someone has got to keep an eye on the meter.

Training and Change Management

Teams used to Power BI will need training on new Fabric features (Spark notebooks, pipeline orchestration, OneLake, etc.). Given the multi-tool complexity of Fabric, investing in upskilling, workshops, or pilot projects will help the workforce adapt. Leadership support and clear communication of the benefits of Fabric will ease the transition for end-users as well as IT staff.

Common Migration Challenges and Pitfalls

Moving from Power BI Premium to Fabric isn’t always smooth. There are plenty of traps teams fall into early on. Knowing what can go wrong helps you plan around it and avoid wasting time (or budget) fixing preventable problems.

Fabric introduces new tools, new architecture, and a different pricing model. That means new skills, planning effort, and real risk if teams go in blind. The pain comes when companies skip the preparation stage.

Tooling Complexity & Skill Gaps

One of the big hurdles with Fabric is the skill gap. It casts a wide net: no single person or team is likely to have it all from the start.

You might have great Power BI and DAX folks, but little to no experience with Spark or Python. That slows things down and leads to underused features.

Mastering Fabric requires expertise across a wide range of tools spanning data engineering, analytics, and BI.

Without serious upskilling, teams risk falling back on old habits, like using the wrong tools for the job or missing what Fabric can actually do.

Steep Learning Curve & Lack of Best Practices

Fabric is still new, and the playbook is not fully written yet. Microsoft offers docs and templates (mostly lifted from Synapse and Data Factory) but there is no built-in framework for how to actually structure your projects. You are starting with a blank slate.

That freedom can backfire if teams wing it without clear guidance. Without predefined standards, organizations have to create their own rules: workspace setup, naming conventions, data lake zones, all of it.

And until that settles, most teams go through a trial-and-error phase that slows things down.

Fragmented or Redundant Solutions

Fabric gives you a few different ways to do the same thing, like loading data through Pipelines, Dataflows, or notebooks. That sounds flexible, but it often leads to confusion. Teams start using different tools for the same job, without talking to each other. That is how you end up with duplicate workflows and zero visibility. Unless you set clear rules on what to use and when, things drift fast.

Capacity and Licensing Surprises

Fabric doesn’t use fixed capacity like Power BI Premium. It runs on compute units: scale up, down, pause. You pay for usage.

Sounds fine. Until you get the bill.

Teams pick F32 to save money. But anything below F64 drops free viewing. Now every report needs a Pro license. Under Premium? Included. Under Fabric? Extra cost. And most teams don’t see it coming.

Plenty of companies that switched to F32 thinking they were optimizing costs got hit later with Pro license expenses.

Want the same viewer access as P1? You’ll need at least F64. That can cost 25–70% more, depending on setup.

There are ways to manage it (annual reservations, Azure commit discounts) but only if you plan before migration. Not after.

Data Refresh and Downtime Considerations

The mechanics of migrating workspaces are straightforward (reassigning workspaces to the new capacity), but there are operational gotchas. When you migrate a workspace, any active refresh or query jobs are canceled and must be rerun, and scheduled jobs resume only after migration. If not carefully timed, this could disrupt data refresh schedules. Customers may need to “recreate scheduled jobs” or at least verify them post-migration to ensure continuity. Planning a hybrid migration (running old and new in parallel) can mitigate disruptions.

Resource Management Pitfalls

Fabric lets you pause or scale capacity. Sounds like a good way to save money. But when a capacity is paused, nothing runs — not even imported datasets. Reports go dark.

Companies with global teams or 24/7 access needs quickly learn: pausing overnight isn’t an option.

There’s another catch: all workloads share the same compute pool. So if a heavy Spark job or dataflow kicks off, it can choke your BI reports unless you plan around it.

Premium users didn’t have to think about this: those systems were separate. Now it’s on you to tune compute (CUs), schedule jobs smartly, and monitor usage in real time.

Ignore that, and you’ll hit capacity walls: slow reports, failed jobs, or both.

Pricing and Licensing Differences

One of the biggest changes in moving to Fabric is the pricing and licensing model. Below is a comparison of key differences between Power BI Premium (per capacity) and Microsoft Fabric.

Capacities and Scale

Power BI Premium: Fixed capacity tiers P1–P5 (e.g. P1 = 8 v-cores). No smaller tier below P1. Scaling requires purchasing the next tier up.

Microsoft Fabric: Flexible capacity sizes (F2, F4, F8, F32, F64, F128, …). Can choose much smaller units than old P1 if needed. Supports scaling out or pausing capacity in Azure portal.

Included Workloads

Power BI Premium: Analytics limited to Power BI (datasets, reports, dashboards, AI visuals, some dataflows). Other services (ETL, data science) require separate Azure products.

Microsoft Fabric: An all-in-one platform. Includes Power BI (equivalent to Premium features) plus Synapse (Spark, SQL), Data Factory, real-time analytics, OneLake, etc. Superset of data capabilities.

User Access Model (A Critical Difference)

Power BI Premium: Unlimited report consumption by free users on content in a Premium workspace (no per-user license needed for viewers).

Microsoft Fabric: Unlimited free-user consumption only on F64 and above. Smaller SKUs require Pro/PPU licenses for viewers.

On-Premises Report Server

Power BI Premium: Power BI Report Server (PBIRS) included with P1–P5 as dual-use right.

Microsoft Fabric: PBIRS included with F64+ reserved capacity. Pay-as-you-go SKUs need separate license.

Purchase & Billing

Power BI Premium: Purchased via M365 admin center as subscription (monthly/annually). Fixed cost. Not counted toward Azure commitments.

Microsoft Fabric: Purchased via Azure (Portal or subscription). Pay-as-you-go or reserved. Eligible for Azure Consumption Commitments (MACC).

Cost Level (Capacity)

Power BI Premium: P1 = $4,995/month. Higher SKUs scale linearly (P2 ~$10k, P3 ~$20k).

Microsoft Fabric: F64 = ~$8,409.60/month pay-as-you-go. F32 = ~$4,204.80/month. More features included.

Scaling and Pausing

Power BI Premium: No dynamic scaling. Capacity is always running. No pause option.

Microsoft Fabric: Can scale up/down or pause capacity in Azure. Pausing stops charges but also suspends access.

Future Roadmap

Power BI Premium: per capacity is being phased out (no new purchases after 2024; sunset in 2025).

Microsoft Fabric: is the future. All new features (Direct Lake, Copilot, OneLake) are in Fabric.

Key takeaways on pricing/licensing

Existing Power BI Premium customers will need to transition to an F SKU at their renewal (unless on a special agreement). In doing so, they should prepare for potential cost increases at equivalent capacity levels, although Fabric’s flexibility (smaller SKUs or scaling down) can offset some costs if used wisely.

The benefits of Fabric’s model include more granular scaling, alignment with Azure billing (useful if you have Azure credits), and access to a broader set of tools under one price.

The downsides include complexity in cost management and the need to adjust to Azure’s billing cycle.

Careful analysis is recommended to choose the right capacity (F SKU) so that performance and user access needs are met without overspending.

Use Cases and Success Stories of Fabric Migration

Several organizations have already made the leap from Power BI Premium to Microsoft Fabric. These real-world case studies highlight the motivations for migration and the benefits achieved.

Flora Food Group — Consolidation and Real-Time Insights

Flora Food Group, a global plant-based food company, was juggling Synapse, Data Factory, and Power BI as separate tools. Too many moving parts. They decided to consolidate everything into Fabric.

The move wasn’t rushed. They ran Fabric alongside their legacy stack and started with the big datasets. They used a medallion architecture (bronze-silver-gold) in OneLake to build a single source of truth.

From there, the upside came fast:

Unified setup — reporting, engineering, science, and security in one stack
Better reporting — centralized semantic models made data reuse easy
Direct Lake — killed the need for scheduled refreshes; reports now pull fresh data near real time
Lower waste — idle compute from one workload now powers another
Faster BI teams — integrated tools meant fewer handoffs and less prep time

According to their Head of Data & Insight, the migration simplified their architecture and cut costs, while boosting capability. They see it as a strategic step toward what’s next: AI-powered analytics with Fabric Copilot.

BDO Belgium — Scalable Analytics for Mergers & Acquisitions

BDO Belgium was hitting walls with Power BI Premium, especially during M&A due diligence, where speed and clarity are non-negotiable.

So they built a new analytics platform on Fabric. They called it Data Eyes.

The shift paid off:

Faster insights — better performance on large, complex datasets
Self-service access — finance teams explored data without writing code
One interface — familiar to users, powerful at scale
Simpler backend — IT maintains one platform, not a patchwork

Fabric gave them what Power BI alone couldn’t: a system that handles scale and puts data in the hands of non-technical users.

For BDO, it wasn’t just an upgrade; it changed how the business works with data.

Other Early Adopters

Many organizations that were already invested in the Microsoft data stack find Fabric a natural progression.

Some companies reported that Fabric’s unified approach streamlined their data engineering pipelines and BI. They cite benefits like reducing data duplication (thanks to OneLake) and easier enforcement of security in one place rather than across multiple services.

Fabric’s integration of AI (Copilot for data analysis) is seen as an advantage.

The pattern is that companies migrating from Power BI Premium experience improvements in data freshness, collaboration, and total cost of ownership when they leverage the full Fabric ecosystem of tools.

Value comes from utilizing Fabric’s broader capabilities rather than treating it as a like-for-like replacement of Power BI Premium.

Organizations that approach the migration as an opportunity to modernize their data architecture (as Flora did with medallion architecture and real-time data, or BDO did with an intuitive analytics app) tend to reap the most benefits. They achieve not just a seamless transition of existing reports, but also new insights and efficiencies that were previously difficult or impossible with the siloed tool approach.

Implications of Not Migrating to Fabric

Given Microsoft’s strategic direction, companies that choose not to migrate from Power BI Premium to Fabric face several implications in terms of features, support, and long-term viability.

Feature Limitations

Fabric isn’t just the next version of Power BI. It’s a superset. Staying on Power BI Premium means missing the features Microsoft is building for the future.

No OneLake. No Direct Lake. No unified data layer. No Spark workloads. No Copilot. No built-in AI. Those are Fabric-only.

If you stay on Premium, your analytics stack stays frozen. Fabric keeps evolving: with deeper integration, faster performance, and cloud-scale features.

You can bolt on Azure services to replicate some of it, but that means extra setup, extra cost, and more moving parts.

Support and Updates

Microsoft is ending Power BI Premium per capacity SKUs. New purchases stop mid-2024. Renewals end in 2025.

What that means: you’ll need to move to Fabric if you want to keep using the platform.

There’s a temporary bridge: existing Premium customers can access some Fabric features inside their current capacity. But that’s a short-term patch. Not a strategy.

Once your legacy agreement runs out, so does your support. No new features. No roadmap. Just a countdown to disruption.

Fabric is the future. Microsoft’s made that clear.

Potential Cost of Inaction

Delaying Fabric may seem easier in the short term, but the cost shifts elsewhere.

Power BI Report Server won’t be bundled once Premium SKUs are retired. It will require separate licensing through SQL Server Enterprise + SA.

Fabric also consolidates multiple tools (ETL, warehouse, reporting) into a single platform. Staying on the old stack means paying for them separately.

Microsoft is offering 30 days of free Fabric capacity during transition. After that, migration gets more expensive and less flexible.

Long-Term Roadmap Alignment

After 2025, support for legacy Premium issues could slow down — because engineering focus will be on Fabric. Eventually, the Power BI Premium brand itself may disappear.

Holdouts will face a bigger, messier migration later: with more change to absorb, less time to adapt.

Early movers get the opposite: smoother transition, room to adjust, and a seat at the table. Microsoft is still shaping Fabric. Companies that migrate now can influence what comes next.

Choosing not to migrate to Fabric is not a risk-free stance. In the immediate term (for those with existing Premium deployments), it means missing out on new capabilities and efficiencies. In the medium term (by 2025), it becomes a support risk as the old licensing model is phased out. While organizations can continue with Power BI Pro or Premium Per User for basic needs (these are not impacted by the capacity SKU retirement), larger scale analytics initiatives will increasingly require Fabric to stay on the cutting edge.

Therefore, companies should weigh the cost of migration against the cost of stagnation. Most will find that a planned migration, even if challenging, is the prudent path to ensure they remain supported and competitive in their analytics capabilities.

Building Smarter APIs: What Every Developer Should Know About API Gateways

Dmitry Baraishuk — Mon, 25 Aug 2025 19:50:00 +0000

An API gateway is the main element of the API architecture that simplifies API integration and management of API requests. API gateways are situated between a client and backend services and help coordinate their communication. They also centralize and ease API management and ensure compatibility of modern and legacy systems.

Building APIs is easy — managing them at scale is not. That’s where API gateways come in. For 20+ years, Belitsoft, a software development company, has been solving these challenges for teams integrating multiple services. Here, we delve into the reasons gateways are important, their benefits to performance and security, and the errors you should avoid when deploying them.

What Is an API Gateway?

An API gateway is located between a client and a set of backend services, which improves the integration between them. This is a tool that serves as the single entry point for the client. The client entering this point may be an application or device, e.g., a single-page application, a mobile application, an internal system, or a third-party service or system.

Two elements of the API gateway are control and data planes. Those elements can be bundled together or deployed independently. The control plane serves as an interface where administrators interact with gateways and determine routes, policies, and necessary data. The data plane is the setting where the incoming requests are handled according to the rules of the control plane. It routes network traffic, uses security policies, and generates logs or measures for tracking.

An API gateway applies policies for user authentication, request frequency limiting, and timeout/retry mechanisms. It also offers metrics, logs, and data to monitor performance, find troublesome issues, and analyze usage.

Why Use an API Gateway?

There are several key areas where API gateways become helpful.

An Adapter and a Facade: Enhancing System Flexibility

An API gateway provides an interface for engineers to interact with backend services. It should be flexible and understandable. All the parts of the system should be connected, but not heavily dependent on each other for the architects to be able to change some components without breaking the whole system. At the same time, the elements should serve a common goal. From the client’s perspective, they also use the API gateway as an interface to communicate with backend services. This way, an API gateway is like a facade that simplifies communication with the system. If the backend systems change, be it a location, architecture, or language, the API gateway adapts to those changes and clients do not feel the difference.

Orchestrating Backend Services

Sometimes it is necessary to gather the APIs of several backend services into a single client-facing API. It simplifies API consumption for frontend engineers, reduces the complexity of the backend, and improves request routing. A client may need to address several backend services. Doing this one by one is time-consuming. Orchestrating multiple calls to several independent backend APIs is faster and more convenient for a client. The results from backend services are gathered and transferred to a client in a single response.

Defending from Security Threats

An API gateway is the point of users’ first interaction with an API backend. Hackers can also be among those users. Huge enterprises typically have multiple security-focused measures such as web application firewalls (WAF), content delivery networks (CDN), dedicated demilitarised zones (DMZ), perimeter networks, etc. Smaller organizations also protect their API gateways with security-focused functionality. The following measures are cost-effective in dealing with unauthorized access, DDOS attacks, and excessive resource usage: authentication and authorization rules, monitoring and logging, HTTPS/TLS encryption, IP allow and deny lists, TLS termination, rate limiting, or load shedding for high-traffic scenarios.

Observing the API Consumption

Being at the edge of the system and receiving the majority of user requests, an API gateway provides important data about the application performance and customer satisfaction levels. The gateway enables monitoring of key performance indicators (KPIs) such as customer conversion rates, streaming initiation rates, revenue per hour, and detection of accidental or deliberate API abuse. It is a location to monitor the number of errors and throughput and to annotate requests that are transferred further through the system. All this data is important for further analysis and insights generation. The observability strategy usually implies dashboards and visualizations for correct interpretation of the metrics and alerting functionality for proactive issue resolution.

Managing API Lifecycle

Both internal and external parties use APIs. Large organizations develop an API strategy with goals, limitations, and resources set. A complete API lifecycle includes various stages, such as planning, designing, developing, testing, promoting, and others. Engineers and developers interact with API gateways during multiple of those stages. Besides, user traffic passes through the gateway. That is why implementing a relevant API gateway is critical.

Enabling Monetization

Often, the APIs that are available to customers are developed as products. They are provided together with account management functionality and payment options. Modern enterprise API gateways allow for monetization. It is realized with such solutions as Apigee Edge and 3Scale. These portals integrate with PayPal or Stripe. Customers can set up rate limits, quotas, and consumption options to control the API usage.

Where Is an API Gateway Deployed?

For startups, small and medium-sized companies, an API gateway is usually located at the edge of the system. It might be the edge of the data center or cloud. In such a situation, a single API gateway guides users to the backend services.

For enterprises, an API gateway is situated in multiple locations, as it is a component of a product, line, business, or department. Therefore, the gateways become separate implementations and provide different functionality in accordance with requirements and possibilities, e.g., operating on devices with limited processing power.

Subtypes of API Gateways

There is no exact agreement about the classification of API gateways in the software development domain. Different industry segments demand different things and, consequently, there are different views about an API gateway. That is why several subtypes of API gateway may be discussed.

Traditional Enterprise Gateways: Such API gateways are used to manage business-focused APIs. These gateways are integrated with API lifecycle management solutions and help to release, operate, and monetize APIs at scale. There are open-source solutions and commercial versions available on the market. However, they rely on additional services like databases. Those databases have to be reliable so as not to disrupt the gateway’s operations. Maintaining those dependencies adds expenses and should be taken into account in disaster recovery (DR) and business continuity (BC) plans.
Microservices Gateways: They direct inbound traffic to backend APIs and services. They focus on tasks like routing, security, and traffic control and are not used for API’s lifecycle management. They are deployed as separate components and often use an underlying platform, e.g., Kubernetes, for scaling and maintenance.
Service Mesh Gateways: This is a type of gateway that handles basic traffic management tasks. That is why they mostly lack enterprise features, such as integration with identity or authentication solutions.

Common API Gateway Pitfalls

There are some API gateway pitfalls that developers should try to avoid.

Sometimes organizations need the service mesh functionality. They route the traffic through the API gateway. However, it may lead to performance and security troubles and demand additional expenses, as cloud vendors charge egress fees. Another problem is insufficient scalability, which causes a gateway overloading.
Many API gateways supplement their functionality by creating plugins and modules. Such features as logging or filtering are useful. However, if the whole business logic is put into plugins, it couples the gateway with services or applications. This may result in a fragile system, i.e., a change in the plugin impacts the whole organization. Besides, in such a situation, the release of the target service is deployed together with a plugin.
Multiple API gateways are usually deployed in large organizations. It is done to segment departments or networks. It may become a problem though if there is a necessity to release a simple service upgrade. It requires the coordination of many gateway teams and the performance is negatively affected.

Beyond GPT: How Specialized Financial LLMs Power Modern Finance

Dmitry Baraishuk — Mon, 25 Aug 2025 09:56:01 +0000

A major expectation in banking is that LLMs can serve as intelligent assistants for both customers and employees. In the world of asset management, including hedge funds, mutual funds, and other investment firms, the interest in financial LLMs centers on gaining an informational edge and productivity boost. Fintech companies and startups view financial LLMs as an opportunity to differentiate their products with AI and to build new services faster. When the insurance industry speaks of a financial LLM (sometimes explicitly an "insurance LLM"), they mean a model that can understand insurance-specific language and workflows. Companies that provide financial data, analytics, and news have been quick to explore LLMs, often coining their solutions as financial LLMs to market their domain expertise. Their perspective is that a financial LLM should function as an expert financial analyst that a user can talk to on demand.

Belitsoft, a custom software development company, has been building reliable financial solutions for EU, UK and US clients since 2014. Our services now extend to full-cycle LLM implementation, covering everything from selecting architecture and configuring infrastructure to fine-tuning models with domain-specific data and integrating them into enterprise workflows, always with strict data security in mind. We build models that deliver strong contextual accuracy for compliance, trading, and customer experience, all while maintaining a secure environment.

What Is a “Financial LLM”?

A financial LLM is a large language model, trained or fine-tuned on financial data, to be tailored for the finance domain, able to answer questions or generate content with an understanding of financial context, instruments, and regulations.

Such models grasp industry jargon (tickers, regulations, accounting terms), handle numeric and tabular context, and comply with financial regulations in their outputs.

Organizations seek to apply the power of GPT-style models to banking, markets, insurance, and financial analytics while incorporating domain expertise and control. General-purpose LLMs (like GPT-4) lack certain finance-specific knowledge or precision, and companies have begun developing specialized “FinLLMs”.

BloombergGPT was one of the first large models trained specifically on a wide range of financial data (in addition to general text).

Core Features and Capabilities of Financial LLMs

Financial LLMs can answer questions, analyze and summarize text, classify sentiment or intent, check compliance, and produce financial writing.

Financial LLMs are used to generate content tailored to finance needs: draft research reports, write personalized portfolio explanations for clients, compose client emails, or generate financial news articles. Such a model writes in a style and context that financial professionals and customers expect.

Question Answering on Financial Knowledge

It's a chat assistant that understands your world and speaks your financial language, backed by actual data.

Financial LLMs help answer questions like “What happened in Company X’s Q3 results?” or “What does Basel III actually require?” not by guessing, but by pulling answers from internal docs, or research.

They’re built to understand finance, and talk like a human, whether you’re a banker checking policy, or an investor tracking the market.

Most financial LLMs now prioritize auditability, because in this space you need to show where the answer came from. No black box. Just traceable output linked to source.

Document Summarization and Report Generation

It summarizes lengthy financial documents (research reports, earnings call transcripts, 10-K filings, insurance policies) into concise, clear narratives.

A financial LLM produces an executive summary of a 100-page annual report or distills key points from an earnings call in a few sentences. This is a highly valued feature given the volume of texts.

JPMorgan’s internally-developed DocLLM is designed to process visually complex documents and extract key information, providing summaries and answering questions about the content.

Automating report generation (writing first drafts of market commentary or credit memos) is another capability of LLM.

Sentiment Analysis and Market Insights

LLMs are getting better at pulling signals from fast sources like news, Twitter, and analyst notes.

They can tag headlines or posts as positive, negative, or neutral for a stock. That's basic for a fintech LLMs.

Regulatory Compliance and Risk Assessment

Finance is heavily regulated. LLMs in this space need to support compliance and risk, not just generate text. Most real deployments use retrieval augmentation or guardrails to keep answers accurate and policy-aligned.

FinLLMs are used to cross-check text against rules - for example, scan loan docs for compliance issues, flag SEC or FINRA violations, and pull policy red flags from internal communications.

Financial LLMs are also used for risk checks. They parse financial statements, credit history, and reports to surface red flags or consolidate exposure data.

Domain-tuned models are safer, because they stay within boundaries: no leaks, no speculation, no policy violations.

Financial Data Extraction and Synthesis

Extracting structured data from unstructured financial text is another core capability.

An LLM ingests a pile of earnings reports or claim forms and pulls out key fields (revenues, dates, loss amounts, etc.), performing data entry and aggregation.

These models can then synthesize data across sources by aggregating and comparing data from multiple quarterly reports to answer “How did revenue grow quarter-over-quarter?”.

They can fill out templates or spreadsheets with information gathered from documents. This capability supports use cases like automating due diligence (consolidating data on a company from various filings) and feeding downstream analytics or models.

FinBERT (financial sentiment analysis)

FinBERT is a specialized open-source BERT-based model trained on financial text (news, filings, social media) for sentiment analysis.

FinBERT was released years ago and hasn’t been actively updated, but 2024 year’s paper shows that it’s still useful, especially when fine-tuned and combined with a time-series model like LSTM.

FinBERT hasn’t been updated in years, however this 2024 paper shows it’s still usable when fine-tuned and combined with other models like LSTM. FinBERT is based on BERT, trained on financial text to work as a sentiment classifier, not a full LLM by current standards.

The study shows it still holds as a reliable component inside a larger pipeline. If you work with financial news and need sentiment signals, you can fine-tune it on your own data and feed the output into whatever model you already use (forecasting, scoring, classification).

Load the model, run inference on news or filings, and map the output to positive, neutral, or negative. Output can be used as a feature in trading logic: entry/exit signals, risk filters, portfolio weighting. Use cases: news sentiment on equities, regulatory sentiment for risk exposure, general signal extraction from contracts or disclosures.

For example, FinBERT can be used with QuantConnect, a cloud platform for developing, testing, and deploying algorithmic trading strategies across equities, FX, futures, options, derivatives, and crypto.

FinGPT (financial sentiment analysis)

FinGPT is an open-source financial large language model (LLM) developed by the SecureFinAI Lab at Columbia University for sentiment analysis, market trend prediction, and financial report summarization. FinGPT is a model built using transformer architecture.

The model itself hasn't been updated since 2023 due to lack of funding, but it's still being actively used. For example, in 2025, there was news about fine-tuning this model to do extra tasks like financial risk prediction via audio analysis or end-to-end trading.

FinGPT v3.3 shows that a fine-tuned open-source model can outperform GPT-4 and earlier domain-specific models like FinBERT on narrow financial tasks without needing GPT-4 scale or cost.

How to Test Financial Systems the Right Way: Compliance, Security, and Scale

Dmitry Baraishuk — Fri, 22 Aug 2025 08:27:49 +0000

In financial systems, you’re not testing if your automation script ran green, but whether the system still moves money, handles identities, calculates correctly, and doesn’t trigger a compliance call. It is about protecting revenue, enforcing compliance, and avoiding regulator fallout. Whether you are building something new or trying to keep legacy from breaking, this is what real QA looks like.

Belitsoft is a QA and software testing partner with 20+ years of experience helping companies maintain product stability. Our quality assurance automation engineers understand how financial systems work - from transactions to compliance - so we know what needs to be tested, and why it matters. From preparing safe test data to making sure your software is ready to launch, we take ownership of the QA process, so you don’t have to worry about gaps.

What Testing Really Means in Finance

From small advisory firms to global banks, the priorities are the same: protect funds, manage risk, stay ahead of regulation. Behind that is software. Financial institutions run on it. If the software fails, the business fails.

That includes everything from CRM systems to investment platforms, Big Data analytics, compliance tools, and audit systems. New apps get added fast. Legacy systems stay around. The result is complexity. When something breaks, you do not just lose a transaction, you lose customers, revenue, and legal standing.

Banking and financial services are now technology. Core functions, from onboarding to risk modeling to KYC, are entirely digital. Billions of transactions happen daily across web and mobile. Most of that rides on infrastructure that is expected to be flawless or thoroughly tested.

A bug in a payment gateway is not just about UX. It may cause financial loss, trigger regulatory review, and generate fines. A logic error in interest calculation can result in misstatements that require remediation and disclosure.

With mobile-first banking, digital account origination, and real-time transactions, release cycles are measured in days, not quarters. QA teams are expected to deliver full coverage under pressure: faster than ever, with less tolerance for error.

Financial systems don’t just show content. They move money, manage identities, enforce regulatory logic. They handle authentication, fraud controls, settlement flows, and trading pipelines. One missed condition in a rule engine or calculation logic, and you’re explaining it to compliance.

What Users Expect — And What Systems Must Prove

End users are not reading about uptime SLAs. They are managing retirement accounts, trading ETFs mid-flight, checking budgets poolside. If your app stalls, breaks, or delays - you don’t lose a click, you lose trust.

Mobile banking has now surpassed internet banking. These apps are not companion tools. They are the primary financial control center for millions. They must operate flawlessly under real-world usage: load spikes, regional handoffs, API throttling, identity checks and fraud detection logic. Testing has to cover that. And not just the happy path: the broken sessions, the dropped packets, the compliance edge cases.

The Core Challenges of Testing Financial Software

Sensitive Data Cannot Be Treated Like Test Data

Financial applications run on personal, high-value, regulated data. Testing teams cannot use raw production data. That’s not just a best practice - it’s a compliance issue.

Testing environments require anonymized or synthetic datasets that behave like real data, but carry zero exposure risk. That means format-valid, domain-accurate, and traceable - not a redacted spreadsheet someone exported and forgot to delete.

Masking, anonymization, and secure data provisioning are prerequisites. Without them, you're building test coverage on a legal liability.

Scale Makes It Worse

Most financial applications don’t operate in simple workflows. They're multi-tiered, highly concurrent, and designed for both real-time and batch processing. High throughput is the baseline.

You’re not testing an app. You’re testing encrypted transactions, large-scale user sessions, real-time pricing engines, API chains, audit and recovery logic, reporting and compliance logs, data warehousing under retention requirements.

Domain Knowledge Is Not Optional

This is where most generic QA fails. Testers in finance need to understand how money moves. That includes: FX conversions, settlement flows, lending logic, risk scoring, trading workflows, KYC/AML paths, regulatory edge cases.

You cannot validate a risk engine, pricing model, or loan approval rule without knowing what the system should do - not just what the spec says. Domain fluency is a baseline, not a bonus.

The Cost of Mistakes Is Measured in Real Money

Every missed bug has downstream consequences: security breaches, broken compliance audits, product delays, user attrition, regulator inquiries, missed SLA thresholds, financial exposure.

Types of Software Testing In Financial Services

Functional & Integration Testing

Functional Testing

You test the application logic. Does it work: calculate, follow the rules, etc.? That means account creation, fund transfers, loan approvals, payment execution, dashboard reporting, etc. Each test confirms the platform behaves exactly as it should for users, regulators, and auditors.

Integration Testing

No system runs alone. Financial apps pull from CRMs, loan engines, identity providers, fraud tools, payment processors and gateways, trading platforms, merchant systems, internal APIs, bureaus, regulators. If the data doesn’t flow, the system fails, even if the UI looks fine. Integration testing checks data synchronization across systems, error handling when a dependency fails, secure transmission of PII, response time under normal and peak conditions, etc.

Compliance Testing

You’re not testing features. You’re testing whether the system can survive an audit.

Financial platforms operate under constant regulatory pressure - SOX, PCI DSS, GDPR, Basel III. Every release has to prove compliance.

The QA suite must validate the things that regulators will ask about:

Data Privacy. Encryption, masking, and PII protection across environments
Audit Trails. Transactions, edits, and events - all timestamped, tamper-proof, and queryable
Transaction Integrity. Every calculation is accurate, consistent, and reproducible
Access Controls. Role-based restrictions that work everywhere, not just in production
Disaster Recovery. Failover plans that work. Backups that restore. Evidence that both are tested.

Automation helps, but only if it’s built to cover real regulatory logic. "Green test results" mean nothing if your coverage misses data residency, privacy enforcement, or cross-border compliance checks.

Compliance isn’t static. Frameworks change. New rules show up. You need testing that adapts - and proves that your system can handle it.

If you're operating across multiple jurisdictions, the rulebook multiplies: RBA, ASIC, APRA (Australia), CFPB, FTC (USA), RBNZ, FMA, Privacy Commissioner (New Zealand), SBV, SSC (Vietnam), etc.

Each body brings its own review process, documentation requirements, and audit expectations - all tied to the financial product, region, and user group.

You don’t pass this with a checklist. You pass it with a system that holds up under scrutiny.

Security Testing

Financial systems handle everything attackers want: personal data, payment flows, authentication tokens, and the logic that controls payouts.

Tests must cover it and simulate abuse. That means forcing credential misuse, invalid session tokens, malformed transaction payloads to prove resistance.

Security checks don’t start when code is "ready". They run with it. Penetration tests need to be continuous. Encryption has to be verified with inspection, not assumed because the config file says so. Permitted functions are accessible and restricted functions are not accessible according to the user’s role or job title.

Performance and Stress Testing

Financial platforms fail under pressure: market spikes, month-end loads, concurrent sessions, salary disbursements. Performance testing confirms system behavior under real load.

Most high-volume systems don’t crash in staging, but in production. Load testing helps prevent that by simulating production behavior before production is involved.

Stress testing pushes that further. You don’t guess where it breaks: you find it out. That includes high-volume transfers, simultaneous logins, batch events. Any time the traffic goes high.

Latency testing measures response times for real-time trading/payments.

You test for concurrent users under volatile traffic, system limits under forced degradation, and how fast the system recovers when it buckles.

Disaster Recovery Testing

Backups aren’t counted until they’re restored cleanly. Failover isn’t accepted until it happens under real load.

Specialized Financial Testing

Risk scenario testing for simulating market crashes, fraud attempts and liquidity crises.

End-of-Day batch testing for overnight processing validation.

Reconciliation testing for ensuring that transactions are matching across ledgers, bank statements and reports.

Currency and Localization testing for multi-currency handling, tax rules and regional compliance checks.

Regression Testing

Release cycles are shrinking. The pressure to deliver is constant. Every feature added introduces more paths, more data conditions, and more opportunities for something to break in a way that no UI script will ever catch.

Every update, patch, or release adds risk. Regression testing is how you contain it.

You’re proving that nothing critical broke in the process of adding new features, especially in revenue-producing or compliance-bound flows. Existing features must still function, logic paths must remain stable, no silent failures, no downstream side effects, no audit-triggering regressions in reporting or calculation.

A modular, evolving regression suite keeps coverage aligned with the platform. It needs to grow with the product, and not get replaced sprint by sprint.

Regression testing is the only way to ship with confidence when the system is already in production.

Test Automation for Financial Services

Manual testing can't track change at the speed it happens, and it doesn’t scale. By the time you’ve finished validating one release, three more are coming.

If a test runs every release, it should already be automated.

That’s where automation comes in place: core workflows, high-risk paths, anything repeatable. Run it nightly or trigger it in the pipeline.

Test automation tools simulate real users, log everything, run tests in parallel, and don’t get tired.

But this isn’t about full automation. That’s a fantasy. You automate what pays off. Everything else waits. Financial systems are expensive to test. So it’s done in stages. Modular.

Test Automation for Regression and API testing as well as AI/ML based testing for anomalies in transaction patterns detection (fraud detection) are considered to be the best practice for Automation in Financial systems.

Testing Process for Financial Services

Use a structured QA workflow: adaptable, but strict.

Analysis & Planning. Understand business rules, compliance needs and risks. Define the scope, set objectives, and pick tools.
Test Design & Case Development. Write the test cases. Map data requirements. Confirm coverage against regulatory and technical baselines.
Environment Setup. Build isolated, compliant test environments. Mirror production architecture as closely as possible.
Execution. Run the tests. Manual and automated. They run, they log, and failures get tracked to root.
Issue Resolution. Fix defects. Verify the fixes. Use regression to confirm nothing else broke while patching.
Retesting. Re-run the failed paths. Validate fixes hold. Check for downstream issues introduced by the change.
Release Approval. Everything passed? Logs clean? Coverage holds? Vulnerabilities mitigated? Then you ship.
Audit and Continuous Improvement. Strengthen future testing by learning from past gaps: review critical bugs that slipped through, update your test repository.

What Financial QA Teams Bring

Testing financial systems requires more than automation coverage. Yes, automation frameworks help. Yes, shift-left practices reduce risk. But unless the test team understands what matters, and how failure propagates downstream, they’re just running checklists.

Financial QA means building coverage that’s audit-proof, risk-aware, and designed for volatility.

Internal teams may be overloaded, or unfamiliar with the testing domain. Testing gets squeezed between delivery targets and audit timelines. That’s where external QA partners come in.

When internal teams hit their limit, outside specialists do more than fill gaps. Firms that offer software testing for financial services bring:

Domain-aligned engineers who know what to test and what not to miss
Custom strategies, not templates
End-to-end test ownership, including test data management, security controls, and field-level reconciliation
Tool expertise across modern stacks: mobile, desktop, API, cloud, data-heavy systems

QA in this space includes everything from test case design to release signoff and risk ownership through each phase.

Why Enterprise Vibe Coding Could Replace SaaS

Dmitry Baraishuk — Thu, 21 Aug 2025 19:57:00 +0000

As more companies use generative AI tools to let non-programming staff create internal applications, many narrow SaaS products that charge per user can be replaced with solutions built in house.

AI coding gives teams a lot of power, but without experienced engineers steering the work, that power can backfire. Inadequate oversight puts projects at risk of becoming a tangled mess of security flaws, concealed bugs, and code that no one is willing to maintain. Belitsoft — a development company with over 20 years in engineering and AI integration that applies structured, expert-led workflows to ensure quality, security, and maintainability in AI-assisted projects and can reduce previously high development costs for clients.

Internal Vibe Coding: Will Enterprises Buy Fewer SaaS Applications?

Artificial intelligence coding platforms (Lovable, Bolt, Replit, Cursor, etc.) are rapidly transforming enterprise software strategy. By converting simple, natural language prompts directly into working code, these tools reduce the difference between adopting a SaaS subscription and coding your own solution to nothing. As the time, cost, and complexity of DIY development drop, the value proposition of traditional subscription-based SaaS comes into question. Companies that master AI development can control their software, reduce spending, and accelerate innovation.

Low Barriers

In many cases, it is already faster to build a custom tool with AI than to learn a vendor’s complex user interface. Non-programming AI builders - often business or operations specialists - can now create working software after only weeks of training. This growing talent pool, combined with the productivity boost from AI for every developer, makes projects that once seemed uneconomical suddenly possible.

The first areas to move from buying to building are lightweight but highly customized.

These include HR and training portals, Q&A and knowledge bases, revenue operations dashboards, CPQ calculators, AI-driven healthcare tools (like AI medical coding software), and custom marketing tools.

Security team can replace a SaaS-based survey with an internally built alternative using Bolt. A revenue operations staff member can code a pricing calculator that would have once required commercial software. A recruiter can use the Lovable platform to create an interview training course. Managers can write even their own AI-based personal CRM, sidestepping the Salesforce interface completely.

Incumbent vendors feel the pressure. While the core Salesforce customer record database may stay popular, the profitable add-ons built on top of it are now vulnerable. Salesforce’s response - its new Agentforce suite - shows early promise. Many smaller and mid-tier SaaS providers do not have similar options.

Challenges

Enterprise-level reliability, security, and ongoing maintenance do not go away just because code is machine-generated. When an AI-written application fails, it is not always clear who owns the fix.

To reduce that risk, AI-building platform providers provide standard stacks that include authentication, staging, security controls, and data access patterns. This lets companies move prototypes into production without hiring large teams of senior engineers.

Bright New Future?

Firms that use AI tools early will gain speed, flexibility, and cost advantages. Those that rely only on SaaS could end up paying more for less.

On the other hand, software vendors must build unique AI features or face the risk of being replaced.

Overall SaaS spending still rises. Companies keep buying cloud software, just a different mix. Heavyweight systems stay SaaS - ERP, finance, payroll, global CRM databases remain too complex or regulated to rebuild quickly - those contracts keep renewing. However, some CEOs already predict a shake-out in which only the largest or most AI-advanced SaaS vendors survive, while the rest are folded into broader hubs.

Some SaaS Was Never Hard to Build

Before AI-assisted vibe coding tools appeared, most business-to-business software development was slow rather than technically difficult.

Many engineering teams spent weeks or months recreating features every SaaS product needs, such as user-permission matrices, audit logs, and email notification systems, even though thousands of developers had solved these problems before. Eighty percent of an engineer’s time went into this repetitive work, while the truly difficult challenges - understanding customers, designing the right workflow, and scaling the architecture - competed for the remaining bandwidth.

Generative AI coding platforms such as Cursor, Windsurf, Loveable, and Replit change that equation. By recycling proven open-source patterns and boilerplate, they reduce build times for standard features by at least half and often by as much as five times.

A user-permission service that once took three weeks now appears in three days. An audit log drops from two weeks to half a day. Email scaffolding is ready in hours.

These tools do not yet create a fully hardened, enterprise-grade product overnight, but they eliminate the “artificial slowness” that used to dominate business-to-business development. Since well over ninety percent of SaaS functionality is routine, the impact is broad.

Product teams can test several workflow designs in the time it once took to build one, refining decisions with real feedback. Engineering is no longer the bottleneck. Requests that used to trigger three-month roadmap debates now become three-week sprints, and internal panels or admin consoles are ready by Friday.

For founders and engineering leaders, the question is no longer whether AI will replace developers - it will not. The question is whether their teams will use AI to remove busywork and focus their talent on the problems that matter, such as deeply understanding users, creating scalable systems, and delivering experiences that competitors find hard to copy. Teams that adopt this new approach will reach product-market fit faster and set prices based on differentiated value. Those who do not will still be discussing three-month roadmaps while their rivals are already shipping.

Vibe coding tools mark a fundamental shift, not because they solve the hardest technical problems, but because they remove the slow, repetitive ones that never gave any advantage. Companies that move now will build better products, faster. Those that delay risk watching the market move past them.

More and More Enterprise Software Will Be Assembled with Vibe Coding Techniques

"Vibe coding", the term computer scientist Andrej Karpathy introduced in February 2025 for using large language model tools to generate production code, is quickly rising on the CIO agenda.

Gartner estimates that by 2028, about 40 percent of all new enterprise software will be assembled with vibe coding techniques.

Yet most large organizations remain cautious. The current generation of vibe coding platforms excels at small, temporary projects. For example, a user interface prototype during a hackathon or a celebratory web page in minutes. Such experiments succeed in sandboxes, proofs of concept, and disposable utilities.

In these cases, the code does not need to be highly robust, scalable, or built to last. Those qualities are exactly what enterprises need for customer-facing or critical systems, and analysts agree the tools are not there yet. Security controls, audit trails, and large-scale deployment patterns are still being developed. CIOs say they welcome AI-driven productivity, especially during multiyear cloud and ERP migrations, but insist they will not compromise on enterprise-grade reliability.

A March 2025 HackerRank survey found that more than two-thirds of engineers feel extra pressure to deliver faster since AI assistants became part of the tool chain. Gartner expects about 80 percent of developers to reskill by 2027 as generative AI changes their roles, shifting work from writing boilerplate code to reviewing, securing, and integrating AI-generated output.

Analysts urge CIOs to keep vibe coding projects in controlled, well-governed environments, set clear security, compliance, and testing standards, and keep close communication with engineering teams to decide where this approach fits best.

Large language models are improving quickly, and Omdia predicts noticeable quality improvements within six to twelve months, so the readiness gap may close sooner than expected. Until then, organizations that pair strong governance with targeted pilots can gain early productivity benefits without taking on unknown risk.

Scaling Vibe Coding in Enterprise IT

Enterprises are experimenting with vibe coding - the practice of using large language model tools to generate working software with minimal hand coding - because it promises rapid prototyping and shorter release cycles. Thanks to readily available LLM APIs, GitHub Copilot, and similar assistants, projects that once required a full-stack team can now be kicked off by analysts or subject matter experts.

The difficulty emerges when organizations try to scale those prototypes for everyday, nontechnical users. Choices that feel harmless in a sandbox can turn into long-term liabilities. Extending a Python Flask demo to a full web product, for instance, collides with the reality that most modern front-end tooling, hosting frameworks, and pretrained AI agents gravitate toward React and TypeScript stacks. Are you ready for this?

Two distinct modes of vibe coding

Senior architects use AI as a force multiplier: they prompt multiple agents, explore design forks, and evaluate trade-offs quickly. Casual enthusiasts, by contrast, may generate code on demand and ship it unchecked.

Both practices produce very different outcomes and risk profiles. Without oversight, the second mode can scatter inconsistent applications and unforeseen technical debt across the enterprise.

Architecture and economics

Hosted models such as OpenAI’s deliver sub-three-second responses that local open-source models struggle to match without substantial GPU investment. Firebase accelerates back-end build-out by more than 60 percent compared with Kubernetes, but its usage-based billing can become volatile once user numbers rise. Each platform or data-store decision ripples through cost forecasts, latency budgets, and support models, demanding active monitoring and a clear exit strategy.

For CTOs, vibe coding shifts the bottleneck from writing code to deciding what should be built, how it should be hosted, and whether the result is operationally sustainable. Deep technical judgment becomes more valuable, because the keyboard is no longer the scarce resource - architectural clarity is.

Scaling safely requires rigorous product management: centralized governance committees, reference architectures for would-be vibe coders, scoped feature requests, scheduled technical-debt reviews, and explicit security and compliance checkpoints. Cultural enablers matter too - structured upskilling in prompt engineering, psychological safety for experimentation, and guardrails that prevent burnout amid rapid iteration.

Handled well, vibe coding lets enterprises capture innovation speed without the chaos of uncontrolled cloud sprawl. Handled poorly, it simply turbocharges mistakes.

Prompt Engineering in Practice: Building Reliable GenAI Apps with System Prompts

Dmitry Baraishuk — Thu, 21 Aug 2025 08:24:39 +0000

This article contains ideas on customizing prompts for gen AI applications and the golden rules of prompt engineering. The enterprise generative AI market is growing. Companies need “picks and shovels” to adapt LLMs to their proprietary data. To assist businesses, software experts examine their niche terminology and workflows and apply prompt engineering to tailor AI behavior to each customer’s requirements.

Belitsoft is a software development company that offers outsourced services of LLM training. To tailor our customers’ LLMs, we leverage their internal data (policies, documents, workflows) to tailor their LLMs while taking good care of data security. We employ different prompt engineering techniques (few-shot prompting, chain-of-thought, etc.) to align the responses with appropriate use cases. As a result, the LLM interprets complex queries with high contextual accuracy.

Large enterprises seek full-spectrum customization and need LLMs that are trained on their internal knowledge datasets, branding, advanced functionality, etc. These companies can choose to develop a gen AI model from scratch. However, this option requires massive investment. Another way is to either use ready-made models or customize existing ones by training them with proprietary data.

According to market researchers, the global prompt engineering market is projected to grow from USD 280.08 million in 2024 to USD 2,515.79 billion by 2032, with a CAGR of 31.6% during the forecast period.

Why Prompts Are Your Secret Weapon

Interactions between a human and a machine are currently happening with natural language (NL). That is why it is important to formulate prompts that direct artificial intelligence (AI) in its effort to generate relevant and reliable responses. The skill of prompt engineering includes formulating correct requests and anticipating how the AI will interpret and execute commands. Competent prompt engineers build prompts with linguistic precision and the knowledge of how algorithms perform their functions.

No matter what framework you use (LlamaIndex, LangChain, or your own code), the retrieval-augmented generation (RAG) system needs clear, well-structured prompts for every LLM interaction. A RAG-based application behaves as a simple user does while interacting with an LLM through the chat. For every task, such as indexing, retrieval of the information, metadata extraction, or response generation, the RAG system produces prompts. The context is added to those prompts and sent to the LLM.

Ready-made systems like LlamaIndex provide templates, storage, injection, and inspection tools. However, it is necessary to understand and fine-tune them. For instance, in LlamaIndex, every kind of interaction with an LLM uses a default prompt as a template. For example, the TitleExtractor extracts metadata into the RAG workflow. Prompt libraries allow you to speed up the process of creating content and provide a more predictable result since all requests have already been pre-checked. However, the models are regularly updated and it is useful to test the existing prompts on new versions.

Customizing Prompts

The RAG workflow programmatically creates prompts. When LlamaIndex or any other framework is used, it builds prompts based on the company’s documents. The documents are divided into nodes, indexed, and selected with retrievers.

Prompt customization is sometimes necessary or desirable. Developers do it, as it helps to achieve better interaction between the RAG components and the LLM, which leads to improved accuracy, and effectiveness of the app. Prompt customization is used in the following situations:

To integrate domain-specific knowledge and terms
To adjust prompts to a certain writing style or tone
To modify prompts to prioritize certain types of information or outputs
To use different prompt structures in order to optimize performance or quality

The LlamaIndex framework offers the following advanced prompting techniques:

Partial formatting means that you format a prompt partially, leaving some variables to be filled in later. It is convenient for multi-step processes when the required data is not available at once.
Prompt template variable mappings let you reuse existing templates instead of rewriting them.
Prompt function mappings allow for dynamic injection of certain values that depend on some specific conditions.

The Golden Rules of Prompt Engineering

The following golden rules include the prompt’s characteristics, differences of LLMs, and methods of creating prompts. By following these recommendations, you can develop effective and reliable RAG applications using LlamaIndex or other frameworks.

Accuracy

The prompt is precise and does not allow for ambiguity. You will receive a relevant response only if you clearly state what you need.

Directiveness

The directiveness of the prompt impacts the response. The prompt can be either open-ended or specific. The first type implies some space for creativity. The second needs a particular answer. As it was mentioned earlier, prompts combine the static part with dynamically retrieved content. Prompts should contain verbs like “summarize”, “analyze”, or “explain”, because those are clear instructions that make AI understand what is needed.

Context quality

An effective RAG system depends on the proprietary knowledge base. Prompt engineers remove data duplicates, inconsistencies, and grammar mistakes from the database, as they affect the retrieval process and the response generation.

Context quantity

A prompt should be brief and detailed at the same time. It means it should give the context in the amount sufficient to understand the request and specific requirements. Providing a RAG system with more details can give a broader understanding of the task, but it also can confuse the system with a lengthy prompt. Long and unstructured prompts may lead to hallucinations or irrelevant answers. Structured and relevant long prompts can improve accuracy.

Cognitive load is the amount of resources that the LLM needs to examine, understand, and respond to. With RAG systems, cognitive load is the amount and difficulty of the prompt context.

Apart from the context quality and quantity, context ordering is also critical. If you provide a long context, make sure you place the key information at the beginning or at the end. It helps LLMs to extract the main problem from the context and generate a relevant output.

Required output format

You need to specify the output in format, size, or language.

Inference costs

It is important to make cost estimations and consider token usage. Tools like LongLLMLinguaPostprocessor help to compress prompts. Prompt compression techniques can also improve the quality of the final response by removing unnecessary data from the context.

System latency

The system latency is related to the quality of the prompts. When there is a long and overly detailed request, the system requires more time to process it. Long processing times decrease user satisfaction levels. Prompt engineers regularly evaluate the performance of the prompts and optimize them depending on the results. It is a continuous process because the rules are changing rapidly.

Selecting the Right LLM

Not all LLMs are equal. The wrong LLM is able to neglect the effort devoted to crafting prompts. The following characteristics are useful while choosing the model:

Model architecture defines which tasks the model is suited for. Encoder-only models (BERT) categorize texts and predict relations between sentences. Encoder-decoder models (BART) not only understand the input but also generate new texts. They can translate, summarize, and provide responses. Decoder-only models (GPT, LlaMa, Claude, Mistral) predict the next words in a sequence and can perform creative tasks. They write different texts and answer questions. Mixture-of-experts (MoE) models (Mixtral 8x7B) can cope with complex math, multilingual tasks, and code generation.
Model size determines computational costs and the model’s capabilities. The more parameters the LLM has, the more resources it needs, and the higher operational expenses are.
Inference speed is how fast the system processes input and generates output. Model pruning, quantization, and special hardware can improve the LLM speed.

Besides the above-mentioned characteristics, LLMs can be divided according to different tasks or domains, demonstrating better performance in certain scenarios.

Chat models are used for building AI chatbots and virtual assistants.
Instruct models are found in educational tools and productivity applications, where users are interested in a detailed explanation rather than a natural conversation.
Codex models are integrated into development environments and coding automation tools. They help with coding tasks, debugging codes, explaining code snippets, and even generating programs based on a description.
Summarization models transform long texts into short summaries. They are used in news aggregation services, content creation, and research.
Translation models suit global communication platforms, educational platforms for language learners, and localization tools.
Question-answering models are underneath intelligent search engines and interactive knowledge bases.

Methods for Creating Prompts

The following advanced techniques are used for complex and multi-step RAG applications. They structure the input to better guide the model’s internal reasoning.

Few-shot prompting, or k-shot prompting, means showing a couple of examples of the task. Those examples demonstrate the model of what kind of response is expected from it. It helps to optimize the system to the tasks specific to a certain niche.
C**hain-of-Thought (CoT) prompting **is breaking the problem into several steps. Instead of asking the system to provide the final result, prompts encourage the model to explain the process step-by-step. For example: “Children have five apples. John has eaten two apples, and Mary has eaten one apple. How many apples are left? Explain the solution step-by-step”. The system shows each calculation one by one as a school student does. The process of answer generation becomes transparent and reliable with this method.
Self-consistency method improves the performance of the CoT prompting. It generates several reasoning paths and selects the answer that appears in most or all cases. For example, the task about apples mentioned earlier can be solved in three ways:
5–2–1 = 2 apples left
5 — (2 + 1) = 2 apples left
5–1–2 = 2 apples left
The answer is 2 in all approaches, so 2 is the final result. This method of prompting is used to solve logic puzzles, math problems, and real-world reasoning (“Should I buy the shares of this company?”).
Tree of Thoughts (ToT) prompting is based on the CoT, but it goes further, generates several ways of dealing with each step, and evaluates the results of each step. It may turn back if the result is incorrect and examine another solution. Therefore, each solution is like a new branch of a tree.
Prompt chaining is giving short prompts in a sequence. The output of the first prompt becomes the input of the second. It makes the process of dealing with complicated tasks simpler.

API-Based and Tool-Augmented Prompting

The following methods are used when the model interacts with external systems, tools, or APIs to retrieve or process data.

Function Calling is calling an external function according to the described scheme (via the OpenAl API, etc.). The model provides structured outputs (e.g., JSON) after calling integrated APIs. For example, in response to “Weather forecast in Paris?” the model calls getWeather(“Paris”) and generates the answer.
Tool Use allows the model to dynamically choose tools (search engines, calculators, APIs, etc.) while generating the answer. For example, to provide the latest news on a certain topic, it uses a connected search tool. Models retrieve live data and verify facts.
ReAct (Reason + Act) combines natural language reasoning and tool execution. Users provide prompts such as “I need to find out the dollar exchange rate. First tell me what you’re going to do, and then do it.” The model gives a step-by-step plan, performs actions (tool calls), observes the results, and continues its logic. ReAct serves as the foundation for AI agents and retrieval-augmented decision-making.

.NET Core + React in 2025: What Developers Need to Build Secure, Scalable Apps

Dmitry Baraishuk — Wed, 20 Aug 2025 20:29:00 +0000

Companies from various industries rely on .NET Core (now the unified .NET platform) for secure, high-speed backend, and on ReactJS for responsive frontend. Together, the pair scales, ticking the boxes for performance and compliance. To make that picture real, hiring teams hunt for engineers who can craft APIs, juggle data workflows, and shape good interfaces. All of it has to play with industry rules - HIPAA, PCI DSS, you name it - before the product sees daylight.

Belitsoft, a software development company with over 20 years of experience, is the partner to call when .NET and React need to do the heavy lifting - in any domain. From HIPAA and PCI to MES and Kafka, our teams turn modern stacks into production-ready platforms that work, scale, and don't fall over on launch day. This guide details what to consider when hiring .NET Core and React developers in 2025, covering skills and patterns for building secure, responsive, and scalable applications, whether you're forming an internal team or working with an external partner.

Healthcare Use Cases

Hospitals, clinics and insurers now build and refresh software on a two-piece engine: .NET Core behind the scenes and React up front.
Together they power seven daily arenas of care.

Electronic records. Staff record demographics, meds and lab work through React dashboards that talk to .NET Core APIs. The same server side publishes FHIR feeds so outside apps can pull data, while React folds scheduling, imaging and results into a single screen. One large provider already ditched scattered tools for a HIPAA-ready .NET Core/React platform tied to state and federal databases.

Telemedicine. Booking, identity checks and data routing live on .NET Core services. React opens the video room, chat and shared charts in the browser. An FDA-cleared eye-care firm runs this way, with AI triage plugged into the flow and the server juggling many payers under one roof.
AI diagnostics and decision support. .NET Core microservices call Python or ONNX models, then stream findings over SignalR. React paints heat-mapped scans, risk graphs and alert pop-ups. The pattern shows up in everything from retinal screening to fraud detection at insurers.

Scheduling and patient portals. .NET Core enforces calendar rules and fires off email or SMS reminders, while React gives patients drag-and-drop booking, secure messaging and live visit links. The same front end can surface AI test results the moment the backend clears them.

Billing and claims. Hospitals rebuild charge capture and claim prep on .NET Core, which formats X12 files and ships them to clearinghouses. React grids let clerks tweak line items, and adjusters at insurers watch claim status update in real time, complete with AI fraud scores.

Remote patient monitoring. Device data streams into .NET Core APIs, which flag out-of-range values and push alerts. React clinician dashboards reorder patient lists by risk, while React Native or Flutter apps show patients their own vitals and care plans.

Mobile health. Most providers and payers ship iOS/Android apps - or Progressive Web Apps - built with React Native, Flutter or straight React. All lean on the same .NET Core microservices for auth, records, claims and video sessions.

Developer Capabilities to Expect in Healthcare

Developers must speak fluent C#, ASP.NET Core middleware, Entity Framework and async patterns, plus modern React with TypeScript, Hooks and accessibility know-how.

They wire up OAuth2 with IdentityServer, juggle FHIR, HL7 or X12 data, and push live updates over SignalR.

Front-end work often rides on MUI or Ant Design components, Redux or Context state, and chart libraries such as Recharts or D3. Back-end extras include logging with Serilog, health checks, background workers and calls to Python AI services.

Delivery depends on Docker, Kubernetes or cloud container services, CI/CD pipelines in Azure DevOps or GitHub Actions, and infrastructure code via Bicep, Terraform or CloudFormation. Pipelines run unit tests (xUnit, Jest), static scans and dependency checks before any release.

Security and compliance sit at the core: TLS 1.2+, encrypted storage, least-privilege roles, audit logs, GDPR data-rights handling, and regular pen-testing with OWASP tools. Domain know-how - FHIR resources, SMART auth, DICOM imaging, IEEE 11073 devices and insurer EDI flows - rounds out the toolkit.

With that mix, teams can ship EHRs, telehealth portals, AI diagnostics, scheduling systems, billing engines and RPM platforms on a single, modern stack.

FinTech Use Cases

Banks and fintechs lean on a .NET Core back end and a React front end for every critical job: online banking, real-time trading and crypto exchanges, payment handling, insurance claims, and fraud dashboards.

Finance demands uptime, airtight security and millisecond latency, so the stack is deployed as micro-services in an event-driven design that scales fast and isolates faults.

A typical setup splits Accounts, Payments, Trading Engine and Notification services - they talk by APIs and RabbitMQ/Kafka. When the Payments service closes a transaction, it emits an event that the Notification service turns into an alert. .NET Core's async model plus SignalR streams live prices or statuses over WebSockets to a React SPA that tracks complex state with Redux / Zustand and paints real-time charts through D3.js or Highcharts. All traffic is wrapped in strong encryption, while Identity or OAuth2 enforces MFA, role rules and signed transactions.

U.S. banks are modernizing legacy back ends this way because .NET Core runs on Windows, Linux and any cloud. They ship the services to AKS or EKS clusters in several regions behind load balancers and fail-over, staying up 24 × 7 and auto-scaling consumers at the opening bell. The result: a stable, fast back end and a flexible, secure front end.

Developer Capabilities to Expect in FinTech

Back-end engineers need deep C#, multithreading, ASP.NET Core REST + gRPC, SQL Server / PostgreSQL (plus NoSQL for tick data), TLS & hashing, PCI-DSS, full audit trails and Kafka / RabbitMQ / Azure Service Bus.
Front-end engineers bring solid React + TypeScript, render-performance tricks (memoization, virtualization), WebSockets / SignalR, visualization skills, big-data handling and responsive design.

Domain fluency (trading rules, accounting maths, SOX and FINRA) keeps algorithms precise and compliant - a rounding slip or race condition can cost millions.

Reliability rests on Docker images, Kubernetes, CI/CD (Jenkins, Azure DevOps, GitHub Actions) with security tests, blue-green or canary rollout, Prometheus + Grafana / Azure Monitor, exhaustive logs, active-active recovery and auto-scaling.

Teams work Agile with a DevSecOps mindset so every commit bakes in security, operations and testing.

E-Commerce Use Cases

In U.S. e-commerce - retail sites, online marketplaces, and B2B portals - .NET Core runs the back end and React drives the front end.
The stack powers product catalogs, carts, checkout, omnichannel platforms, supply-chain and inventory portals, and customer-service dashboards.

Traffic bursts (holiday sales) are absorbed through cloud-native deployments on Azure or AWS with auto-scaling.

A headless, microservice style is common: separate services handle catalog, inventory, orders, payments, and user profiles, each with its own SQL or NoSQL store.

React builds a SPA storefront that talks to those services by REST or GraphQL.

Server-side rendering or prerendering (often with Next.js) keeps product pages SEO-friendly. Rich UI touches - faceted search, live stock counts, personal recommendations - rely on React Context, hooks, and personalization APIs.

Events flow through Azure Service Bus or RabbitMQ - an order event updates stock and triggers email.

Secure API calls to Stripe, PayPal, etc., plus Redis and browser-side caching, cut latency. CDN delivery, monitoring tools, and continuous deployment keep the storefront fast, fault-tolerant, and easy to evolve.

Developer Capabilities to Expect in E-Commerce

Back-end engineers design clear REST APIs, model domains, tune SQL and NoSQL schemas, use EF Core or Dapper, integrate external payment/shipping/tax APIs via OAuth2, apply Saga and Circuit-Breaker patterns, enforce idempotency, block XSS/SQL-injection, and meet PCI by tokenizing cards.

Front-end engineers craft responsive layouts, manage global state with Redux or React Context, code-split and lazy-load images, and deliver accessible, cross-browser, SEO-ready pages.

Many developers switch between C# and JavaScript, debug both in VS/VS Code, and partner with designers using Agile feedback loops driven by analytics and A/B tests.

DevOps specialists automate unit, integration, and end-to-end tests (Selenium, Cypress), wire CD pipelines for weekly updates, run CDNs, and watch live metrics in New Relic or Application Insights.

Logistics & Supply Chain Use Cases

Logistics firms wire their operations around a .NET Core back-end and a React front-end so every scan, GPS ping or warehouse sensor reading appears instantly to drivers, dispatchers and customers.

The system pivots on four core apps - route-planning, package tracking, warehouse stock control and analytics dashboards.

Devices publish events (package-scanned, truck-location, temperature-spike) onto Kafka/RabbitMQ, microservices such as Tracking, Routing and Inventory pick them up, update records in SQL, stream logs to a NoSQL/time-series store, run geospatial maths for best routes, and push notifications.

React single-page dashboards - secured by Azure AD - subscribe over WebSocket/SignalR, redraw maps and charts without lag, cluster thousands of markers, and keep working offline on tablets in the yard.
Everything runs in containers on Kubernetes across multiple cloud regions - new pods spin up when morning scans surge.

The event-driven design keeps components loose but synchronized, so outages are isolated, traffic spikes are absorbed, partners connect via EDI/APIs, and the supply chain stays visible in real time.

Developer Capabilities to Expect in Logistics & Supply Chain

Teams that ship this experience blend real-time back-end craft with front-end visual skill. .NET engineers design asynchronous, message-driven services, define event schemas, handle out-of-order or duplicate messages, tune SQL indexes, stream sensor data, secure APIs and device identities, and integrate telematics or EDI feeds.

React specialists maintain live state, wrap mapping libraries, debounce or cluster frequent updates, design for wall-size dashboards and rugged tablets, and add service-worker offline support.

All developers benefit from logistics domain insight - route optimization, geofencing, stock thresholds - and from instrumenting code, so data and BI queries arrive ready-made.

DevOps staff monitor 24/7 flows, alert if a warehouse falls silent, run chaos tests, simulate event streams, deploy edge IoT nodes, and iterate quickly with feedback from drivers and floor staff.

Combined, these skills turn the architecture above from blueprint into a resilient, real-time logistics platform.

Manufacturing Use Cases

Car plants, chip fabs, drug lines, steel mills and food factories all ask different questions, so .NET Core micro-services and React dashboards get tuned to each shop floor.

Automotive. Carmakers run hundreds of work-stations that feed real-time data to .NET services in the background while React dashboards in the control room flash downtime and quality warnings. The same stack drives supplier and dealer portals, spreads alerts worldwide when a part is short, and ties production data back to PLM for recall tracking. Modern MES roll-outs have already slashed defects and sped delivery.

Electronics. In semiconductor and PCB plants, machines spit out sub-second telemetry. .NET services listen over OPC UA or MQTT, flag odd readings, and shovel every byte into central data lakes. React lets supervisors click from a yield dip straight to sensor history. Critical Manufacturing MES shows the model: a .NET core that speaks SECS/GEM or OPC UA and even steers kit directly, logging every serial and test for rapid recall work.

Pharma. GMP rules and 21 CFR Part 11 demand airtight audit trails, which a .NET back-end supplies while React tablets walk operators through each Electronic Batch Record step. Lab systems feed results to the same services and analysts sign off in real time. The stack coexists with legacy software, yet lets plants edge toward cloud MES and predictive maintenance that pings operators before a batch spoils.

Heavy industry. Steel furnaces, presses and turbines still rely on PLCs for hard real-time loops, but .NET gateways now mirror temperatures to the cloud and drive actuators on site. React boards merge furnace status, rolling-mill output and work-orders on one screen. Vibration streams land in micro-services that predict failures; customers see their own machine telemetry in service portals. Containers and Kubernetes let plants bolt new code onto old gear without full rip-and-replace.

Consumer goods. Food and beverage lines run fast and in bulk. PLC events shoot to Kafka or Event Hub, .NET services raise alerts, and React portals put live rates, downtime and quality on phones and wall-screens. Retail buyers place bulk orders through the same front-end, with .NET handling stock, delivery slots and promo logic under holiday-peak load. Batch-to-distribution traceability and sensor-based waste reduction ride the same rails, all on a single tech stack that teams reuse across brands and sites.

Developer Capabilities to Expect in Manufacturing

Back-end developers live in C# and modern .NET, craft ASP.NET Core REST or gRPC services, wire in Polly circuit breakers, tracing, SQL Server, Entity Framework, NoSQL or time-series stores, and speak to Kafka, RabbitMQ and industrial protocols through OPC UA or MQTT SDKs while watching garbage-collection pauses like hawks.

Front-end specialists work in TypeScript and React hooks, manage state with Redux or context, design for tablets and 60-inch screens with Material-UI or Ant, and pull charts with D3 or Highcharts. They keep data fresh via WebSocket or SignalR and lock down every call with token handling and Jest test suites.

DevOps engineers script CI/CD in Azure DevOps or GitHub Actions, bake Dockerfiles, docker-compose files and Helm charts, and keep Kubernetes clusters, Application Insights and front-end performance metrics ticking. Infrastructure as Code with ARM, Bicep or Terraform makes environments repeatable.

Domain know-how turns code into value: developers learn OEE, deviations, production orders, SPC maths and when to drop an ML-driven prediction into the data flow. They guard identity and encryption all the way.
Everyday kit includes Visual Studio or VS Code, SQL studios, Postman, Swagger, Docker Desktop, Node toolchains, Webpack, xUnit, NUnit and Jest. Fans of the pairing say React plus .NET Core gives unmatched flexibility and speed for modern factory apps.

Edtech Use Cases

Schools and companies now lean on a .NET Core back end with a React front end for every major digital-learning task.

The combo powers Learning Management Systems that track courses, content and users, Student Information Systems that control admissions, grades and timetables, high-stakes online-exam portals, and collaborative tools such as virtual classrooms and forums.

These platforms favor modular Web APIs or full micro-services: .NET Core services expose Courses, Students, Instructors and Content - sometimes split into separate services - while React presents a single-page portal whose reusable components (one calendar serves both students and teachers) adapt to every role.

Live chat, quizzes and video classes appear via WebSockets or SignalR plus WebRTC or embedded video, while the back end organises meetings and participants.

Everything sits in autoscaling clouds, so enrolment rushes or mass exams don't topple the system.

Relational databases keep records, blob stores hold lecture videos, and SAS links or CDNs stream them.

REST is still common, but GraphQL often slims dashboard calls.
Multi-tenant SaaS isolates data with tenant IDs and rebrands the React UI at login. The goal throughout is flexibility, maintainability and the freedom to bolt on analytics or AI without disrupting live teaching.

Developer Capabilities to Expect in Edtech

Back-end engineers need fluent ASP.NET Core Web API design, mastery of complex rules (prerequisites, grade maths), solid relational modeling, comfort with IMS LTI, SAML or OAuth single sign-on, and the knack for plugging in CMS or cloud-storage SDKs.

Front-end engineers must craft large, form-heavy React apps, manage state with Redux, Formik or React Hook Form, embed rich-text and equation editors, deliver clear role-specific UX and pass every WCAG accessibility test.

Everyone should handle WebSockets/Azure SignalR/Firebase to keep multi-user views in sync, and write thorough unit, UI and load tests - often backed by SpecFlow or Cucumber - to ensure exams and grading never falter.

On the DevOps side, they automate CI/CD, define infrastructure as code, monitor performance, roll out blue-green or feature-toggled updates during quiet academic windows, and run safe data migrations when schemas shift.

Above all, they must listen to educators and translate pedagogy into code.

Government Use Cases

Across federal and state offices, the software wish-list now starts with citizen-facing portals. Tax returns, benefit sign-ups and driver-license renewals are moving to slick single-page sites where React handles the screen work while .NET Core APIs sit behind the scenes. Internal apps follow close behind: social-service and police case files, HR dashboards, document stores and other intranet staples are being refitted for faster searches and cleaner interfaces.

Open-data hubs and real-time public dashboards are another priority, giving journalists and researchers live feeds without manual downloads.
Time-worn systems built on Web Forms or early Java stacks are being split into microservices, packed into containers and shipped to Azure Government or AWS GovCloud. A familiar three-tier layout still rules, but with gateways, queues and serverless functions taking on sudden traffic spikes. Every byte moves over TLS 1.2+, every screen passes Section 508 tests, and every line of code plays nicely with the U.S. Web Design System, so the look stays consistent from one agency to the next.

Developer Capabilities to Expect in Government

To pull this off, back-end engineers need deep .NET Core chops plus a firm grip on OAuth 2.0, OpenID Connect and, where needed, smart-card or certificate logins. They write REST or SOAP services that talk to creaky mainframes one minute and cloud databases the next, always logging who did what for auditors. SQL Server, Oracle and a dash of XML or CSV still show up in the job description, as do Clean Architecture patterns that keep the code easy to read years down the road.

Front-end specialists live in React and TypeScript, but they also know ARIA roles, keyboard flows and screen-reader quirks by heart. They follow the government design kit, test in Chrome and - yes - Internet Explorer 11 when policy demands it.

On the DevOps side, teams wire up CI/CD pipelines that scan every build for vulnerabilities, sign Docker images, deploy through FedRAMP-approved clouds and feed logs into compliant monitors.

How to Build a Full-Spectrum API Testing Strategy with the Quadrant & Pyramid

Dmitry Baraishuk — Wed, 20 Aug 2025 08:58:11 +0000

A critical part of API development is testing API services. Proper testing allows developers to produce a high-quality product to their clients. Testing enables API developers to guarantee to their customers that the system works under different conditions as expected.

APIs fail to perform consistently, alter, or produce errors with new releases? The cause of such malfunctions is a lack of testing.

Belitsoft is a custom software development company with more than 20 years of experience in building secure, high-performance systems across finance, healthcare, and manufacturing. Our experts in automated testing know how to maintain the balance between sufficient test coverage and confidence in a product and leverage regression testing to safeguard against unintended impacts during updates across financial systems and other critical workflows.

Strategies for Organizing API Testing

The Testing Quadrant

The Testing Quadrant helps arrange tests in the right time and order and not to lose resources. The Quadrant allows for combining technological and business tests.

Technology stands for the correct features. All the parts of the API should work properly and consistently in any situation.

Business testing is making sure the product has been developed according to the customers' needs and goals.

Each of the four quadrants contains certain tests. However, those tests should not necessarily be performed in a particular order

Quadrant 1: Unit and component tests
Quadrant 2: Manual or automated exploratory and usability tests. Requirement refinement
Quadrant 3: Functional and exploratory tests
Quadrant 4: Security tests, SLA integrity, scalability tests

Quadrants 1 and 2 include tests that detect development issues. Quadrants 3 and 4 focus on the product and its possible defects.
The top quadrants 2 and 3 check if the API corresponds to users' requirements. The bottom quadrants 1 and 4 contain technology tests, i.e., internal issues of the API.

When a team is developing an API, they apply tests from all four quadrants. For example, if a customer needs a system for selling event tickets that can handle high traffic, the testing should start from the fourth quadrant and focus on performance and scalability. Automated testing is preferable here, as it provides faster results.

The Testing Pyramid

Another strategy for arranging API testing is based on the Testing Pyramid.

The Testing Pyramid demonstrates how much time and expenses unit tests, service tests, and UI tests require

Unit tests are cheaper and easier to conduct than end-to-end tests. Unit tests are the base of the Pyramid. They relate to the Quadrant 1 from the previous strategy. Unit tests include testing small separated parts of code. They check if each "brick" of the construction is solid and reliable.

Service tests are more complex and, therefore, slower than unit tests. They require higher maintenance costs due to their complexity. Service tests check the integration of several units or the API with other components and APIs, that is why they are also called integration tests. Service testing allows developers to verify if the API responds to requests, if the responses are as expected, and if the payloads are returned as expected. The tests are taken from the Quadrants 2 and 4.

End-to-end tests are the most complicated. They focus on testing the whole application from the start to the endpoint and includes interactions with databases, networks, and other systems. End-to-end tests demand many resources for preparation, creation and maintenance. They also run slower than unit or service tests. End-to-end testing allows developers to understand that the whole system is performing well with all the integrations. These tests are situated at the top of the Pyramid because they perform at low speed with high costs and their proportion should be much smaller in comparison with unit tests.

Some teams use low-maintenance, scriptless tools, such as Katalon, for automating regression testing within end-to-end scenarios to reduce effort required to maintain complex test scripts.

From the perspective of a project owner, end-to-end tests seem to be the most informative. They simulate the real process of interaction with an API and demonstrate tangible results if the system works.

However, unit tests should not be underestimated. They check the performance of smaller parts of the system and allow developers to catch errors in the early stages and fix them with minimum resources.

Testing the API Core: Unit Testing

The main characteristics of unit tests are their abundance, high speed, and low maintenance costs.

When testing separate parts of the API, developers feel confident that their "bricks" of the construction are correct and operate as expected. If we develop an API for booking doctor's appointments, the "bricks" of the unit testing might be the following:

Correct authentication of patients
Showing relevant slots in doctors' schedules
Appointment confirmation and related updating of the schedules

Unit tests are self-contained, as they are run independently, do not rely on other tests or systems, and provide transparent results. If the test fails, it is easy to detect the reason and correct it.

Sometimes tests are written before the code. This style of development is known as Test Driven Development (TDD). This way, tests guide the development process. It allows developers to know what their code should result in beforehand and write it in a clean and well-structured manner. If the code is changed and the implementation breaks, tests quickly catch the errors.

An outside-in approach is a way to perform TDD. With this approach, developers ask questions about the expected functionality from the user's perspective. They write high-level end-to-end tests to make sure the API brings users the results they wish. Then, they move inwards and create unit tests for individual modules and components. As a result, developers receive a bunch of unit tests that are necessary on the ground level of the Testing Pyramid. This approach saves developers time as they do not create unnecessary functionality.

Tuning Parts Together: Service/Integration Testing

While developing an API, it is important to confirm responses that match expected results. Service testing verifies how the API operates and how it integrates with other systems.

Service tests are divided into two groups:

Component tests for internal checks
Integration tests for checking external connections with databases, other modules, and services

Component testing is conducted to see if the API returns correct responses from inbound requests. Tests from Quadrant 1 verify if all the parts of the API work together. Automated tests from Quadrant 2 validate the right responses from the API, including rejecting unauthorized requests.

For example, to test the authentication component of the API that books doctors' appointments, the following endpoint should be tested:

When sending an unauthorized request, the response should return an error of 401 (Unauthorized)
When an authenticated user sends a booking request, a successful response of 200 (OK) is sent

Integration testing allows developers to verify the connections between the modules and external dependencies. However, it is not practical to set up the whole external system for this test. That is why only the communication with external dependencies is checked. Thus, bringing the whole database of authorized patients to check its dependency with the booking API would become an end-to-end test, not an integration.

Contract testing allows conducting integration testing while building an API. Tested interactions save developers' time and guarantee compatibility with external services. To put it simply, the contract is an agreement that describes the rules of interaction between two entities. For example, when a patient books a doctor's appointment, the contract specifies how the booking API interacts with the authentication service or patient database. Developers use contract testing to verify whether those interactions happen according to the rules set.

Testing for Vulnerabilities: Security Testing

Security testing stands in Quadrant 4 and is also a very important part of API development.

API specialists perform various types of security API tests such as Authentication & Authorization, Input Validation, Business Logic Flaws, Sensitive Data Exposure, Rate Limiting & Throttling (to prevent Brute force, DoS attacks), Transport Layer Security, Error Handling, Endpoint Security (only required HTTP methods are used), Dependency and Configuration, WebSocket & Real-Time API Testing.

For the booking API from our example, they ensure that the whole doctor's schedule or the information about other patients can't be captured by malicious users or "attackers".

Checking the Entire Functionality: End-to-End Testing

Finally, we have reached the top of the pyramid. We are using automated testing from the Quadrant 2 as a part of End-to-End execution. This approach verifies core cases and confirms that the systems work together and give correct responses.

To test an external API that should interact with multiple third parties, it is not realistic to copy those systems and simulate how their UIs work. It would be a waste of time. That is why it is recommended to set test boundaries. For example, for our booking API, necessary services, such as authentication service, might be included in testing, while other external dependencies like messaging systems are excluded. This way, the tests will target the most critical functions of the system and will not require additional time.

Another important point in organizing end-to-end testing is using realistic payloads. Large payloads may cause the APIs to break. Developers should know who their consumers are.

REST vs GraphQL vs gRPC: A Comparative Analysis

Dmitry Baraishuk — Tue, 19 Aug 2025 19:30:00 +0000

Application Programming Interface (API) allows developers and clients to conveniently communicate with backend services and receive the responses they expect. REpresentational State Transfer (REST) APIs are among the protocols used by such companies as Amazon, Netflix, Uber, Spotify, Salesforce, and others to integrate different services and data and improve user experience. They also apply gRPC, GraphQL, and other protocols for specific needs.

Belitsoft is a custom software development company with more than 20 years of experience in building secure, high-performance systems across finance, healthcare, and manufacturing. Our engineers specialize in designing and scaling APIs — from REST and GraphQL to gRPC — using C#, ASP.NET Core, and modern DevOps practices.

Why APIs Matter?

API-based architecture is characterized by the abstraction of implementation detail. This means that developers can make quick changes, update, or replace components in the back end, and the consumers will not be impacted as the API contract does not change.

Modern microservices architectures and service-oriented architectures lead to rising numbers of separate services. Those services may run simultaneously. Therefore, there is a necessity for developers to coordinate the processes and address the challenges of distributed communication. A variety of API protocols, including REST, gRPC, and GraphQL assist software developers in solving those issues. However, it is essential to know the differences between available options and how to choose the right solution for a particular business domain. APIs should be useful tools for DevOps, not a bottleneck or a deployment constraint.

When Is the REST API the Right Choice?

Deciding which API standard will be the right one to adopt involves answering the following questions:

What are the other standards that the company has, if any?
Is it possible to extend existing standards to external consumers?
How are consumers impacted by not having a standard?

At Belitsoft, we recommend choosing a standard that best suits the culture of the company and existing API formats. Our developers analyze the current situation and suggest custom API integration with third-party applications if necessary. For example, one of the Belitsoft clients working in the sphere of transportation management spent much time manually processing documents and addressing several applications to check the status of loads, handle insurance, etc. Our experts set the required API integrations with carrier marketplaces, onboarding services, load-tracking apps, accounting platforms, and others. The company automated its main workflows and improved customer service.

REST APIs support both service-oriented and microservice-based architectures. REST APIs use HTTP, which makes them easy to understand and implement. REST was developed as a standard that describes how to interact with a system. For example, GET requests are used to get data, DELETE to remove data, and POST to add data. The client specifies what they want to interact with and the format of data that they expect in the response.

Another characteristic of RESTful APIs is that they are language-agnostic. In combination with standard HTTP methods, it makes REST API an available and low-barrier entry option for both clients and servers.

On the other hand, REST is not strictly typed. For example, the POST request can get data, add, modify, and delete, depending on how the handler of a particular request was implemented. That is why it sometimes brings confusion about what a particular query is used for.

REST vs GraphQL

As with REST, GraphQL works on top of the HTTP protocol and is fully supported by all browsers. That is why both API architecture styles are ideal when developing a web app with a necessity to interact with a backend service or integrate with third-party services. The main characteristics of GraphQL are the following:

GraphQL is an open-source language used to get data for specific fields in a single request to the server. It decreases the number of requests to the server when these fields are stored in multiple entities. However, it requires the developer to know how to build a query correctly in order to get the necessary data.
GraphQL offers a single version across all APIs. It means there is no need for complex management of multiple versions on the consumer side.
GraphQL works best with data and services from a particular business domain. If you have many external, disparate APIs, GraphQL might not be the best choice as it will add complexity.

REST vs gRPC

Remote Procedure Call (RPC) APIs execute codes or functions of other processes. RPC APIs access internal systems and reveal the details to the user. REST APIs hide those details. gRPC is a developing open-source and high-performance RPC framework created by Google for communication between servers or services. Here are the main features of gRPC APIs:

gRPC provides a faster exchange of messages between services, and the messages weigh less, which reduces the amount of data transported through the connection, thus freeing up the connection faster.
The gRPC protocol is typed, i.e., developers create a special file describing all messages and types of data they will send and receive before the implementation. The downside is that modern browsers do not have full support for this protocol, so they usually use an API Gateway. The frontend sends a query to the API Gateway using the HTTP protocol (REST, GraphQL), and the uses gRPC to send messages to the required service to process the request.
REST APIs are stateless, i.e. the requests contain all the necessary data and do not relate to previous interactions. gRPC APIs can be both stateless and stateful. It depends on the implementation.
gRPC allows access to multiple individual functions, but it is not usually used to extend a resource model. REST APIs perform that.
gRPC can be successfully used for high-traffic services and for the two services under tight producer control.

Best Practices and Trade-Offs

When business grows, it becomes necessary to adapt APIs to the changing environment. API versioning is a way to manage REST API alterations without affecting existing integrations.

API Versioning Best Practices

Release a new API version and deploy it in a new location. Legacy applications continue working with an old API version. It is okay for a consumer, as they upgrade to a new location and new API only if they demand new functionality. At the same time, the owner has to maintain all versions of the API and make timely corrections and bug fixes if it is required.
Release a backwards compatible API version. In this situation, it becomes possible to add changes without affecting existing users. Consumers do not need to upgrade the system immediately. However, the downtime should be taken into consideration and the availability of both versions at the time of the upgrade. Even small bug fixes might cause serious issues.
Break compatibility with an old API and ask consumers to upgrade the code. This scenario may bring unexpected interruptions in production. However, sometimes there is no opportunity to avoid compatibility problems with older versions. This is what happened in 2018 when the GDPR (General Data Protection Regulation) was introduced in Europe.

The options mentioned above have advantages and disadvantages for both consumers and API owners. Software development firms like Belitsoft support the combination of those three options. To do that, we use a semantic versioning approach. What does it stand for?

Semantic Versioning

This approach is used in software development to manage versions. It assigns numbers to API releases and divides API versions into three groups.

Major version: This one is non-compatible with the previous API. Consumers have to upgrade to a newer version. They are usually supported by a migration guide and careful monitoring.
Minor version: It is a backwards compatible change with the old API version. Users do not have to change their code.
Patch version: This version does not bring new features or changes to existing functionality. Developers fix bugs and errors with this version.

API Lifecycle

Discussing the API lifecycle with consumers is an important part of API development and integration. Clients understand what to expect if they know the stages that an API passes. A combination of semantic versioning and API lifecycle allows consumers to track the releases of the major APIs leaving minor and patch updates without their participation. Here are the stages of the API lifecycle according to the PayPal Standards:

Planned: This stage is about discussing what you are going to build and what services this API should cover.
Beta: It is the first version of the API to receive consumers’ feedback. Users start to integrate with a new API and provide their ideas for improving it. This stage allows developers to avoid building several major versions in the beginning.
Live: At this stage, the API is in production. Any changes become versions. When a new version appears, the current API becomes deprecated.
Deprecated: Such APIs are not developed any further, but they can be used. When a minor version appears, the API is deprecated only during the validation of the new version. When the new version is validated and compatible with all services, a minor one moves to retired. When a major version comes out, the previous one becomes the retired one. However, it does not happen at once, as consumers need time to migrate.
Retired: The API is not accessible anymore.

Choosing Your 2025 AI Partner: Avoid Mistakes in the Fast-Growing AI Industry

Dmitry Baraishuk — Tue, 19 Aug 2025 09:13:03 +0000

Various artificial intelligence (AI) market research sources claim that the volume of the artificial intelligence market is expected to reach almost 827 billion dollars by 2030 with a CAGR of nearly 28 percent from 2025 to 2030.

AI app creation is the process of developing software apps that utilize AI models and algorithms for analysis of the data, learning from it, predicting, and responding smartly to interactions with users. Machine learning (ML) technology is used for service and innovation driving in various fields, from cybersecurity and legal research to healthcare automation.

Multiple companies around the world provide AI app development services and it can be difficult to select the most appropriate one from the list of popular AI platform software brands. Let's read what some of the major players in the AI app development field have to offer.

Belitsoft — a development company with over 20 years in engineering and AI integration — that apply structured, expert-led workflows to ensure quality, security, and maintainability in AI-assisted projects and can reduce previously high development costs for clients. Their team provides end-to-end generative AI services: from selecting the right model architecture (LLM vs. RAG) and setting up infrastructure (cloud or on-prem), to fine-tuning with domain-specific data, integrating with enterprise systems, and handling testing and deployment.

Top AI Software Development Companies in the USA

Belitsoft

Outsourcing AI development company Belitsoft provides SaaS startups with generative AI development services to create Gen AI apps. When creating an application, developers work in stages: first, the application is designed and experimented with, then built and finally deployed.

During the development stage, programmers research and evaluate models from open-source communities or popular repositories (e.g., Hugging Face). Given the model's performance and size, developers use benchmark tools and different prompting techniques (chain-of-thought prompting, zero-shot prompting, etc.).

When creating genAI apps, Belitsoft developers cut the cost of AI development by using the right frameworks, such as LangChain, and tools.

When deploying, a hybrid "Swiss Army knife" setup approach is preferred - for different use cases different models are used, and cloud infrastructure is combined with an on-premises one to optimize budget and resources.

Once AI-powered apps are launched into production, Belitsoft specialists conduct benchmarking, monitoring, and handling of exceptions thrown by the app.

According to surveys, many companies launch up to several dozen genAI experiments and expect to scale up about a third of their proof-of-concept AI within three to six months. These organizations are from key industries such as technology, financial services, telecommunications and media, healthcare, etc., and/or are advanced in the use of AI. The market demands that business leaders quickly realize the benefits of AI, so the initial prototypes are needed within a few weeks.

The multidisciplinary team consists of data scientists, machine learning (ML) and AI specialists, and ML engineers. Belitsoft software engineers integrate AI into products. They also set up the deployment pipeline. UX designers develop intuitive experiences based on AI.

For small companies and SaaS startups, full-stack AI engineers handle multiple tasks, from developing models to writing front-end code. In the early stages, one or several ML engineers quickly build a prototype for them using public APIs and emerging AI coding practices, which allows startups to follow their budget constraints.

For AI projects, enterprise customers like Fortune 1000 companies get larger cross-functional teams. Belitsoft brings in data engineers for pipelines and data preparation, MLOps engineers for model deployment and monitoring, and security experts.

OpenAI

This company designed ChatGPT - an AI tool that utilizes large language models (LLMs). AI technologies, which are developed by OpenAI, optimize business processes and empower interactions in real time. The company partners with Microsoft, which in turn provides advanced automation systems, virtual assistants, and other secure genAI solutions for different industries.

Microsoft

Microsoft and OpenAI have a long-standing partnership that is backed by billions of dollars in investment. In 2019, Microsoft Azure became the sole provider of OpenAI cloud solutions thanks to this. The company uses machine learning models and AI-powered tools to improve efficiency and productivity in various industries. OpenAI is integrated into Microsoft’s Prometheus model. The company also aims to rebuild its Bing search engine, also known as Copilot, to compete with Google in the search market. In enterprise AI use cases, Microsoft Bing provides real-time automation solutions and advanced AI assistants to optimize workflows.

IBM Watson

IBM clients can make better decisions with the Watson AI product portfolio. IBM offers Watson Studio services for designing and developing AI applications for enterprise clients. The company's solutions include AI apps that improve customer service, simplify workflows, predict outcomes, and reduce costs.

Among the case studies that IBM presented, there is the creation of models for predicting and preventing mortality from sepsis based on clinical data of inpatients. These models have shown high efficiency in situations where time matters and rapid analysis of insurance claims data allows for faster decision-making, for example, on urgent medical interventions.

AWS AI

One of the AI and ML services provided by AWS AI (headquartered in Seattle, USA) is Amazon SageMaker. This solution makes it faster and easier for engineers to build, train, and deploy ML models. Customers across industries use AI and ML tools from AWS AI to personalize, automate, and optimize their business workflows.

Using AI tools, AWS AI customers can improve response rates by creating messages and emails based on the behavior and profile of the prospect. By analyzing service, product, industry, and customer segment, they can create talking points or sales scripts.

Google AI

This branch of Google is engaged in developments in the fields of natural language programming, machine learning, and computer vision. Google AI research and development have led to the creation of Google Cloud AI, Google Translate, and Google Assistant.

Google Cloud’s LLMs technologies and GenAI capabilities transform the fast-food restaurant industry’s customer experience when ordering food in the drive-thru mode. A voice-controlled AI assistant replaces an employee. It processes customers’ voice requests for orders and generates answers to popular questions. Integration with the POS system allows the AI assistant to quickly create an order and send it to the kitchen.

Salesforce Einstein

With AI-powered customer relationship management (CRM) tools, companies can provide personalized customer experiences. They use machine learning, automation, and predictive analytics. Einstein AI’s functionalities enable workflow automation, lead scoring, and sales forecasting.

In particular, sellers can use Salesforce Einstein AI solutions to automatically generate sales pitches for each lead individually. The AI tools use CRM data for phone and email introductory messages.

The assistant bot studies the customer's latest CRM data to prepare or correct an email that matches the lead's needs in context and tone.

Deloitte AI

This professional services firm offers companies from various business industries (government, healthcare, finance, etc.) comprehensive services of AI strategic planning and development. Deloitte AI clients use AI solutions to increase efficiency, automate processes, and improve decision-making.

Generative AI models continuously and simultaneously find discrepancies, patterns, and anomalies and perform a root cause analysis in real time. This is important for risk management processes.

Intel AI

This AI hardware market player offers services and products ranging from advanced AI processors and chips to AI software to help companies in industries such as financial services, cybersecurity, automotive, and healthcare develop and scale AI apps. AI services and products drive progress in real-time data processing and automation, enabling the creation of advanced AI models and effective machine learning.

Which AI Use Cases Show the Most Promise for Companies?

Open access to AI tools has inspired companies to change how they operate and start using GenAI technology in various areas. According to Deloitte research, the IT function occupies a leading position — 28%, operations account for 11%, marketing — 10%, cybersecurity and customer service — 8% each.

In the consumer industry, GenAI apps are used for IT and marketing functions and their volume of GenAI initiatives is 20% each; the volume of customer service initiatives is 12%. In the financial services industry, the most scaled GenAI initiatives are IT (21%), cybersecurity (14%), and finance (13%). In the government industry, IT initiative occupies 96% and operations — only 3%.

Also, Gartner notes that by 2029, the customer service and support industry will be transformed by advanced agentic AI tools, which will autonomously resolve 80% of tasks without human intervention. In this case, generative AI is one of its key components.

According to the Harvard Business Review survey, 89% of respondents expect that AI will become the most transformational technology in a generation.

This drives overall investment in corporate data and AI initiatives. Nearly 99% of companies surveyed say they have increased their investment in AI, with nearly 91% citing it as their top priority. Respondents specify that they see the value of their investment in measurable, quantifiable business results that can be tracked through metrics such as increased productivity and revenue, improved customer acquisition, retention, and increased customer satisfaction.

The percentage of companies allocating from 20 to 39 percent of their overall AI budget to GenAI increased twelve points in 2024.

AI has the potential to shape major areas such as finance, healthcare, cybersecurity, and education.

Fintech Industry

In 2025, the financial sector is shaped by the following trends — AI chatbots for customer service, algorithmic trading, customized financial services, risk assessment, customer authentication, regulatory compliance, transaction optimization, and risk assessment.

Cybersecurity

AI tools enable companies to monitor security in real time, detecting malicious digital footprints, intrusions, and fraud. AI-based software performs predictive and simulation modeling so that the company is prepared for possible attacks from hackers and cybercriminals.

Healthcare

The development of AI is important for various areas of healthcare: chatbots provide initial consultations for patients with mental health problems, and AI-equipped robots ensure precision in complex surgeries. AI tools conduct large-scale data analysis, optimize the management of clinical patient data, identify healthcare trends, and predict possible disease outbreaks.

Moreover, the biomedical and healthcare fields advance with the contribution of AI technologies in medical image analysis, patient diagnosis, personalized drug prescription, follow-up monitoring of treatment progress, drug development, and predictive analytics.

Education

AI technologies enable teachers to create advanced learning materials to make the education process more adaptive and save time. AI tools can analyze students’ progress to identify gaps in their knowledge and adjust their learning, as well as provide students with individual assignments and rewards based on their performance.

How to Choose a Company that Provides GenAI Services?

There are several important criteria for choosing the best AI company: data protection and privacy measures, domain knowledge, experience and expertise, reputation, portfolio and client reviews, and budgetary considerations.

Make sure the company possesses strong data protection controls when you deal with sensitive data.

Having the specific industry or domain knowledge of the company can be a difference-maker in terms of how effective and relevant the artificial intelligence solutions they provide will be. A domain-expert company is more apt to have insights into the exact business problems.

The team's level of experience, as well as credentials and training in AI technologies, ensure effective and comprehensive collaboration. In addition, it is important to evaluate criteria such as the team's working methodologies, adaptability, and availability.

When choosing an AI product development team, it is important to pay attention to its client base and list of completed projects. You can also talk to past or current clients who have sought services from the AI company.

It is important to conduct an in-depth study of the supplier's payment policies, pricing model, warranty policies, quality control guidelines, and delivery timelines.

The agency must be in touch throughout the work process. It has to ensure transparent communication, regular feedback sessions, and joint discussions of adjustments. One of the key indicators of transparent communication is an agile project management methodology with set sprint deadlines, within which the team performs a certain amount of work, reports on the results, and discusses possible improvements with the customer.

An AI solution must grow with the customer’s business and meet increasing demands, so it is important that the artificial intelligence development company chosen is able to scale your project.

After development, an AI product needs maintenance and continuous support. The customer should make sure that the AI team provides appropriate services after development.

Benefits of Collaborating with Companies that Develop AI Software

One of the key benefits of collaboration with top AI development companies is experience and deep knowledge in the field of frameworks and advanced AI algorithms. The client doesn't need to spend time on internal training of their in-house engineers. In addition, there are other benefits.

AI-Powered Solutions Are Tailored to the Client's Business Goals and Objectives

The development company offers customized services and solutions, from designing intelligent recommendation systems for personalized customer experience to developing AI-powered chatbots for communication with users.

Cost-Effectiveness without Additional Budget for Hiring and Training Staff

A company that needs an AI solution building can spend a lot on recruiting and training its internal team. The experience of a third-party AI development company is a more profitable alternative for the client to build its infrastructure.

AI-Powered Technologies Improve the App Development Process

Engineers save time and can focus on more important responsibilities when routine tasks are automated with AI tools. Collaboration with an AI software development company involves the creation of flexible engagement models. The client can order a small project to prove their concept, and if desired, incorporate AI incrementally at the enterprise level.

What Are the Current Trends in AI Development?

When looking at the statistical cross-section of broad AI development trends such as virtual assistants, natural language, machine learning, robotics and process automation, and computer vision, the McKinsey Global Institute projects that by 2030, approximately 70% of all companies will likely have adopted at least one AI tech category. No more than half of the companies will adopt all five categories, and there will be many different companies in between at different stages of AI adoption.

At the average rate of AI adoption, AI is expected to add approximately 13 trillion dollars to the global economy by 2030. This number is equivalent to a 16 percent increase in cumulative GDP compared to today’s level.

AI models are expected to continually adapt and learn from changing conditions, accumulating new skills and knowledge in addition to what they have already learned. Specific future trends include:

More Complex AI Algorithms

AI systems are predicted to use generative models, reinforcement learning, and deep learning to improve efficiency and performance in different tasks.

AI for Edge Computing

With the development and spread of edge computing, AI models and algorithms allow users to lower latency, be less dependent on cloud infrastructure, and process data in real time.

Ethical AI Systems and Addressing Bias

AI systems are designed to be fair, ethical, and include mechanisms to detect and mitigate bias, ensuring equitable and responsible deployment.

Responsible Use of AI

Transparency, accountability, and ethical behavior of AI systems are possible thanks to the creation of best practices for the development, deployment, and utilization of these systems, as well as standards and regulatory policies.

Multimodal AI

In the future, greater interaction and understanding of different contexts will be possible through the integration of data from multiple text, video, audio, and other sources.

Providing a Personalized Experience

AI algorithms will enable personalization across a variety of industries and applications, including content curation, bespoke recommendations, targeted marketing, and adaptive learning.

Explainable AI (XAI)

Today, developers create AI models and/or functions that operate in a more transparent way, explaining their decisions. This increases interpretability and trust from users. This is especially important for financiers, healthcare professionals, and lawyers.

ML and NLP

Advances in NLP mean that AI could respond to cultural subtleties, idioms, and emotions, understanding nuances and context when communicating with a person, rather than just the meaning of words.

Popular NLP apps include AI search, which leverages the power of large language models (LLMs) to improve the way people search for information on the internet. LLMs can answer questions as if they were humans, create and sort text, recognize text in different languages, and translate it from one language to another.

Top .NET MAUI Dev Skills for 2025: Cross-Platform, AI & Industry Apps

Dmitry Baraishuk — Mon, 18 Aug 2025 20:33:00 +0000

Building apps for iOS, Android, Windows, and macOS used to mean separate teams, multiple codebases, and higher costs. .NET MAUI changes that with one shared C#/XAML foundation.

Good MAUI developers know where things break. The Android app may run poorly on older devices, while the iOS version runs well. A banking login can break behind strict firewalls if not engineered with care. Without this expertise, a “single codebase” can quickly become a maintenance headache rather than a time-saver.

Belitsoft is the .NET Maui application development partner when cross-platform is your product’s future. From Xamarin migrations and security-first design to AI/IoT integration and CI/CD automation, Belitsoft’s .NET MAUI developers build modern apps that actually work across platforms and do so securely and scalably. In this article, we’ll look at the skills that set top MAUI developers apart, where the framework adds real business value, and how the right team turns one codebase into reliable, secure apps across platforms.

.NET MAUI Developer Skills To Expect

.NET MAUI lets one C#/XAML codebase deliver native apps to iOS, Android, Windows, and macOS. The unified, single-project model trims complexity, speeds releases, and cuts multi-platform costs while stable Visual Studio tooling, MAUI Community Toolkit, Telerik, Syncfusion, and Blazor-hybrid options boost UI power and reuse.

The payoff isn’t automatic: top MAUI developers still tailor code for platform quirks, squeeze performance, and plug into demanding back-ends and compliance regimes. Migration skills - code refactor, pipeline and test updates, handler architecture know-how - are in demand. Teams that can judge third-party dependencies, work around ecosystem gaps, and apply targeted native tweaks turn MAUI’s "write once, run anywhere" promise into fast, secure, and scalable products.

Core Technical Proficiency

Modern MAUI work demands deep, modern .Net skills: async/await for a responsive UI, LINQ for data shaping, plus solid command of delegates, events, generics and disciplined memory management.

Developers need the full .NET BCL for shared logic, must grasp MAUI’s lifecycle, single-project layout and the different iOS, Android, Windows and macOS build paths, and should track .NET 9 gains such as faster Mac Catalyst/iOS builds, stronger AOT and tuned controls.

UI success hinges on fluent XAML - layouts, controls, bindings, styles, themes and resources - paired with mastery of built-in controls, StackLayout, Grid, AbsoluteLayout, FlexLayout, and navigation pages like ContentPage, FlyoutPage and NavigationPage.

Clean, testable code comes from MVVM (often with the Community Toolkit), optional MVU where it fits, and Clean Architecture’s separation and inversion principles. Finally, developers must pick the right NuGet helpers and UI suites (Telerik, Syncfusion) to weave data access, networking and advanced visuals into adaptive, device-spanning interfaces.

Cross-Platform Development Expertise

Experienced .NET MAUI developers rely on MAUI’s theming system for baseline consistency, then drop down to Handlers or platform code when a control needs Material flair on Android or Apple polish on iOS. Adaptive layouts reshape screens for phone, tablet, or desktop, while MAUI Essentials and targeted native code unlock GPS, sensors, secure storage, or any niche API.

Performance comes next: lazy-load data and views, flatten layouts, trim images, and watch for leaks, choose AOT on iOS for snappy launches and weigh JIT trade-offs on Android. Hot Reload speeds the loop, but final builds must be profiled and tuned.

BlazorWebView adds another twist - teams can drop web components straight into native UIs, sharing logic across the web, mobile, and desktop. As a result, the modern MAUI role increasingly blends classic mobile skills with Blazor-centric web know-how.

Modern Software Engineering Practices

A well-run cross-platform team integrates .NET MAUI into a single CI/CD pipeline - typically GitHub Actions, Azure DevOps, or Jenkins - that compiles, tests, and signs iOS, Android, Windows, and macOS builds in one go.

Docker images guarantee identical build agents, ending "works on my machine" while NuGet packaging pushes shared MAUI libraries and keeps app-store or enterprise shipments repeatable.

Unit tests (NUnit / xUnit) cover business logic and ViewModels, integration tests catch service wiring, and targeted Appium scripts exercise the top 20% of UI flows. Such automation has been shown to cut production bugs by roughly 85%.

Behind the scenes, Git with a clear branching model (like GitFlow) and disciplined pull-request reviews keep code changes orderly, and NuGet - used by more than 80% of .NET teams - locks dependency versions. Strict Semantic Versioning then guards against surprise breakages during upgrades, lowering deployment-failure rates.

Together, these practices turn frequent, multi-platform releases from a risk into a routine.

Security and Compliance Expertise

Security has to guide every .NET MAUI decision from the first line of code. Developers start with secure-coding basics - input validation, output encoding, tight error handling - and layer in strong authentication and authorization: MFA for the login journey, OAuth 2.0 or OpenID Connect for token flow, and platform-secure stores (Keychain, EncryptedSharedPreferences, Windows Credential Locker) for secrets.

All data moves under TLS and rests under AES, while dependencies are patched quickly because most breaches still exploit known library flaws. API endpoints demand the same discipline.

Regulated workloads raise the bar. HIPAA apps must encrypt PHI end-to-end and log every access, PCI-DSS code needs hardened networks, vulnerability scans and strict key rotation, GDPR calls for data-minimization, consent flows and erase-on-request logic, fintech projects add AML/KYC checks and continuous fraud monitoring.

Experience with Emerging Technologies

Modern .NET MAUI work pairs the app shell with smart services and connected devices.

Teams are expected to bring a working grasp of generative‑AI ideas - how large or small language models behave, how the emerging Model Context Protocol feeds them context, and when to call ML.NET for on‑device or cloud‑hosted inference. With those pieces, developers can drop predictive analytics, chatbots, voice control, or workflow automation straight into the shared C# codebase.

The same apps must often talk to the physical world, so MAUI engineers should be fluent in IoT patterns and protocols such as MQTT or CoAP. They hook sensors and actuators to remote monitoring dashboards, collect and visualize live data, and push commands back to devices - all within the single‑project structure.

Problem-Solving and Adaptability

In 2025, .NET MAUI still throws the odd curveball - workload paths that shift, version clashes, Xcode hiccups on Apple builds, and Blazor-Hybrid quirks - so the real test of a developer is how quickly they can diagnose sluggish scrolling, memory leaks or Debug-versus-Release surprises and ship a practical workaround.

Skill requirements rise in levels.

A newcomer with up to two years’ experience should bring solid C# and XAML, basic MVVM and API skills, yet still lean on guidance for thornier platform bugs or design choices.
Mid-level engineers, roughly two to five years in, are expected to marry MVVM with clean architecture, tune cross-platform UIs, handle CI/CD and security basics, and solve most framework issues without help - dropping to native APIs when MAUI’s abstraction falls short.
Veterans with five years or more lead enterprise-scale designs, squeeze every platform for speed, manage deep native integrations and security, mentor the bench and steer MAUI strategy when the documentation ends and the edge-cases begin.

.NET MAUI Use Cases and Developer Capabilities by Industry

Healthcare .NET MAUI Use Cases

Healthcare teams already use .NET MAUI to deliver patient-facing portals that book appointments, surface lab results and records, exchange secure messages, and push educational content - all from one C#/XAML codebase that runs on iOS, Android, Windows tablets or kiosks, and macOS desktops.

The same foundation powers remote-patient-monitoring and telehealth apps that pair with BLE wearables for real-time vitals, enable video visits, and help manage chronic conditions, as well as clinician tools that streamline point-of-care data entry, surface current guidelines, coordinate schedules, and improve team communication. Native-UI layers keep these apps intuitive and accessible. MAUI Essentials unlock the camera for document scanning, offline storage smooths patchy connectivity, and biometric sensors support secure log-ins.

Developers of such solutions must encrypt PHI end-to-end, enforce MFA, granular roles, and audit trails, and follow HIPAA, HL7, and FHIR to the letter while handling versioned EHR/EMR APIs, error states, and secure data transfer.

Practical know-how with Syncfusion controls, device-SDK integrations, BLE protocols, and real-time stream processing is equally vital.

Finance .NET MAUI Use Cases

In finance, .NET MAUI powers four main app types.

Banks use it for cross-platform mobile apps that show balances, move money, pay bills, guide loan applications, and embed live chat.

Trading desks rely on MAUI’s native speed, data binding, and custom-chart controls to stream quotes, render advanced charts, and execute orders in real time.

Fintech start-ups build wallets, P2P lending portals, robo-advisers, and InsurTech tools on the same foundation, while payment-gateway fronts lean on MAUI for secure, branded checkout flows across mobile and desktop.

To succeed in this domain, teams must integrate WebSocket or SignalR feeds, Plaid aggregators, crypto or market-data APIs, and enforce PCI-DSS, AML/KYC, MFA, OAuth 2.0, and end-to-end encryption.

MAUI’s secure storage, crypto libraries, and biometric hooks help, but specialist knowledge of compliance, layered security, and AI-driven fraud or risk models is essential to keep transactions fast, data visualizations clear, and regulators satisfied.

Insurance .NET MAUI Use Cases

Mobile apps now let policyholders file a claim, attach photos or videos, watch the claim move through each step, and chat securely with the adjuster who handles it.

Field adjusters carry their own mobile tools, so they can see their caseload, record site findings, and finish claim paperwork while still on-site.

Agents use all-in-one apps to pull up client files, quote new coverage, gather underwriting details, and submit applications from wherever they are.

Self-service web and mobile portals give customers access to policy details, take premium payments, allow personal-data updates, and offer policy download.

Usage-based-insurance apps pair with in-car telematics or home IoT sensors to log real-world behavior, feeding pricing and risk models tailored to each user.

.NET MAUI delivers these apps on iOS, Android, and Windows tablets, taps the camera and GPS, works offline then syncs, keeps documents secure, hooks into core insurance and CRM systems, and can host AI for straight-through claims, fraud checks, or policy advice.

To build all this, developers must lock down data, meet GDPR and other laws, handle uploads and downloads safely, store and sync offline data (often with SQLite), connect to policy systems, payment gateways, and third-party data feeds, and know insurance workflows well enough to weave in AI for fraud, risk, and customer service.

Logistics & Supply Chain .NET MAUI Use Cases

Fleet-management apps built with .NET MAUI track trucks live on a map, pick faster routes, link drivers with dispatch, and remind teams about maintenance.

Warehouse inventory tools scan barcodes or RFID, guide picking and packing, watch stock levels, handle cycle counts, and log inbound goods.

Last-mile delivery apps steer drivers, capture e-signatures, photos, and timestamps as proof of drop-off, and push real-time status back to customers and dispatch.

Supply-chain visibility apps put every leg of a shipment on one screen, let partners manage orders, and keep everyone talking in the same mobile space.

.NET MAUI supports all of this: GPS and mapping for tracking and navigation, the camera for scanning and photo evidence, offline mode that syncs later, and cross-platform reach from phones to warehouse tablets. It plugs into WMS, TMS, ELD, and other logistics systems and streams live data to users.

Developers need sharp skills in native location services, geofencing, and mapping SDKs, barcode and RFID integration, SQLite storage and conflict-free syncing, real-time channels like SignalR, route-optimization math, API and EDI links to WMS/TMS/ELD platforms, and telematics feeds for speed, fuel, and engine diagnostics.

Manufacturing .NET MAUI Use Cases

On the shop floor, .NET MAUI powers mobile MES apps that show electronic work orders, log progress and material use, track OEE, and guide operators through quality checks - all in real time, even on tablets or handheld scanners.

Quality-control inspectors get focused MAUI apps to note defects, snap photos or video, follow digital checklists, and, when needed, talk to Bluetooth gauges.

Predictive-maintenance apps alert technicians to AI-flagged issues, surface live equipment-health data, serve up procedures, and let them close out jobs on the spot.

Field-service tools extend the same tech to off-line equipment, offering manuals, parts lists, service history, and full work-order management.

MAUI’s cross-platform reach covers Windows industrial PCs, Android tablets, and iOS/Android phones. It taps cameras for barcode scans, links to Bluetooth or RFID gear, works offline with auto-sync, and hooks into MES, SCADA, ERP, and IIoT back ends.

To build this, developers need OPC UA and other industrial-API chops, Bluetooth/NFC/Wi-Fi Direct skills, mobile dashboards for metrics and OEE, a grasp of production, QC, and maintenance flows, and the ability to surface AI-driven alerts so technicians can act before downtime hits - ideally with a lean-manufacturing mindset.

E-commerce & Retail .NET MAUI Use Cases

.NET MAUI lets retailers roll out tablet- or phone-based POS apps so associates can check out shoppers, take payments, look up stock, and update customer records anywhere on the floor.

The same framework powers sleek customer storefronts that show catalogs, enable secure checkout, track orders, and sync accounts across iOS, Android, and Windows.

Loyalty apps built with MAUI keep shoppers coming back by storing points, unlocking tiers, and pushing personalized offers through built-in notifications.

Clienteling tools give staff live inventory, rich product details, and AI-driven suggestions to serve shoppers better, while ops functions handle back-room tasks.

Under the hood, MAUI’s CollectionView, SwipeView, gradients, and custom styles create smooth, on-brand UIs. The camera scans barcodes, offline mode syncs later, and secure bridges link to Shopify, Magento, payment gateways, and loyalty engines.

Building this demands PCI-DSS expertise, payment-SDK experience (Stripe, PayPal, Adyen, Braintree), solid inventory-management know-how, and skill at weaving AI recommendation services into an intuitive, conversion-ready shopping journey.

Migration to MAUI

Every Xamarin.Forms app must move to MAUI now that support has ended: smart teams audit code, upgrade back-ends to .NET 8+, start a fresh single-project MAUI solution, carry over shared logic, redesign UIs, swap incompatible libraries, modernize CI/CD, and test each platform heavily. Tools such as .NET Upgrade Assistant speed the job but don’t remove the need for expert hands, and migration is best treated as a chance to refactor and boost performance rather than a port.

After go-live, disciplined workflows keep the promise of a single codebase from dissolving. Robust multi-platform CI/CD with layered automated tests, standardized tool versions, and Hot Reload shortens feedback loops - modular, feature-based architecture lets teams work in parallel. Yet native look, feel, and performance still demand platform-specific tweaks, extra testing, and budget for hidden cross-platform costs.

An upfront spend on CI/CD and test automation pays back in agility and lower long-run cost, especially as Azure back-ends and Blazor Hybrid blur lines between mobile, desktop, and web.

The shift is redefining "full-stack" MAUI roles: senior developers now need API, serverless, and web skills alongside mobile expertise, pushing companies toward teams that can own the entire stack.