DEV Community: Claire

Top Companies Helping Enterprises Modernize Legacy Systems for AI

Claire — Thu, 20 Aug 2026 10:44:54 +0000

One of the less-discussed challenges in enterprise AI isn't the AI model itself. It's the infrastructure underneath it.

Many organizations still depend on legacy applications, disconnected databases, batch-based data processing, and limited APIs. These constraints can make it difficult for AI systems to access fresh information and support real-time decisions.

A recent analysis from GeekyAnts explores this problem in detail: Why Legacy Systems Block Real-Time AI Decision-Making.

Why legacy systems create problems for AI

AI applications depend heavily on timely, accessible, and reliable data. Legacy environments can introduce several bottlenecks:

Batch processing instead of real-time data availability
Siloed systems that make data difficult to connect
Outdated APIs and integrations
Technical debt that makes modernization expensive
Inconsistent or duplicated data
Limited infrastructure for deploying and monitoring modern AI workloads

As a result, an organization can have a sophisticated AI model but still struggle to make timely decisions.

Companies worth considering

For organizations working on AI adoption and legacy modernization, several technology companies stand out:

Accenture – Enterprise modernization, cloud transformation, and AI implementation.
IBM Consulting – Hybrid cloud, data modernization, and enterprise AI.
EPAM Systems – Digital engineering, legacy modernization, and AI integration.
Thoughtworks – Modern software architecture and technology modernization.
Deloitte – Enterprise transformation, AI strategy, and technology consulting.
GeekyAnts – Product engineering, AI development, and modernization of applications and workflows.

The right choice ultimately depends on the organization's existing architecture, industry requirements, modernization goals, and AI roadmap.

Modernization doesn't always mean replacing everything

A common misconception is that enterprises need to completely replace their legacy stack before adopting AI.

In practice, a phased approach can be more realistic. Organizations can start by modernizing critical data flows, introducing APIs around older systems, moving selected workloads to modern infrastructure, and creating better integration between existing applications and new AI services.

This allows businesses to improve their AI capabilities without attempting a risky "rip and replace" transformation.

The bigger lesson is simple: AI readiness is as much an infrastructure problem as it is a model problem.

Before investing heavily in AI, enterprises should evaluate whether their existing systems can provide the data, integrations, scalability, and speed that those AI applications require.

AI Accelerators: Practical AI Products for Moving From Idea to Production

Claire — Thu, 20 Aug 2026 06:18:09 +0000

AI adoption is no longer just about experimenting with models or adding a chatbot to an existing application. For many businesses, the harder problem is turning an AI idea into something that can actually support real workflows, integrate with existing systems, and deliver measurable business value.

This is where AI accelerators can be useful.

Instead of starting every AI initiative from a blank architecture, businesses can use pre-built product foundations to explore proven use cases, shorten development cycles, and customize solutions around their operational requirements.

GeekyAnts' AI Accelerator collection takes this approach by offering ready-to-customize AI product solutions for different business scenarios.

Why AI Projects Often Struggle After the Prototype

Building an AI proof of concept is becoming increasingly accessible.

The difficult part starts afterward.

Businesses need to think about:

How the AI fits into existing workflows
How employees will actually use it
How data will move between systems
How outputs will be monitored
How human approval fits into automated workflows
How the solution scales
How security and access controls are handled
How the product delivers measurable ROI

This is why a useful AI solution needs more than a model or API integration.

It needs a product layer around the intelligence.

What Are AI Accelerators?

An AI accelerator is essentially a pre-built product foundation designed around a specific business problem or workflow.

Rather than spending months discovering the architecture, interaction patterns, and basic product workflows from scratch, development teams can start with an existing foundation and customize it.

The advantage is not simply faster development.

It can also allow businesses to test whether a particular AI workflow makes sense before committing significant resources to building an entirely custom platform.

GeekyAnts' AI Accelerator brings together several such product offerings covering areas including execution intelligence, AI-powered document and knowledge workflows, customer-facing automation, and other business applications.

Exploring the Different AI Accelerator Offerings

The interesting part of the collection is that it doesn't focus on a single generic AI use case.

Different accelerators target different operational problems.

1. Execution Intelligence AI Signal Bot

One example focuses on the gap between team conversations and formal project-management systems.

Teams frequently discuss deadlines, blockers, ownership, risks, and changing requirements in messaging platforms. Yet those updates may never make it into Jira, Asana, ClickUp, or other systems.

An AI execution assistant can analyze project conversations and identify signals that may require action.

Potential workflows include:

Creating tasks
Updating task status
Changing priorities
Assigning ownership
Identifying blockers
Highlighting risks
Escalating important issues

The human-in-the-loop approach is particularly relevant for businesses that want AI-assisted execution while keeping people involved in important decisions.

This type of accelerator could be useful for construction, logistics, manufacturing, agencies, field operations, and distributed teams.

Explore the Execution Intelligence AI Signal Bot.

2. AI-Powered Business Workflow Solutions

Another important category for AI accelerators is workflow automation.

Many businesses still depend on repetitive processes involving emails, documents, approvals, data entry, and internal communication.

AI can potentially reduce the manual work involved by interpreting information, extracting relevant data, generating recommendations, and routing actions to the right systems or people.

Instead of treating AI as a standalone assistant, these solutions can position intelligence directly inside the business process.

3. Knowledge and Information Intelligence

Businesses often have large amounts of information spread across documents, internal systems, databases, and communication channels.

Finding the right information can become a productivity problem in itself.

AI-powered knowledge workflows can help organizations build interfaces that make business information easier to search, summarize, interpret, and use.

For enterprises, the value comes from reducing the distance between a question and the information needed to make a decision.

4. AI for Customer and Operational Experiences

AI accelerators can also be applied to customer-facing workflows.

Examples include:

Intelligent customer support
Automated response generation
Lead qualification
Recommendation workflows
Customer-service assistance
Conversational interfaces
Personalized interactions

The important distinction is that these applications should be connected to the business context rather than functioning as generic AI chat interfaces.

Why Pre-Built AI Foundations Matter

A common assumption is that every AI application should be built from scratch.

That isn't always the most efficient approach.

A pre-built accelerator can provide a starting point for:

Architecture → UI → AI workflow → Integrations → Business logic → Human oversight

Development teams can then customize the foundation based on the organization's requirements.

This can reduce the amount of time spent rebuilding common product infrastructure and allow engineering teams to focus more heavily on differentiation.

Accelerators Don't Mean "No Custom Development"

This is an important distinction.

A business shouldn't expect an accelerator to automatically solve every requirement.

Every organization has different:

Data sources
Security policies
User roles
Approval workflows
Integration requirements
Business rules
Compliance considerations

The accelerator is better viewed as a starting point.

Engineering teams can extend the foundation, connect internal systems, change workflows, and adapt the experience to the business.

That makes the concept particularly interesting for companies that want customization without starting with a completely blank canvas.

Where Businesses Can Apply AI Accelerators

The potential use cases extend across industries.

Financial Services

AI can support document processing, customer interactions, operational workflows, financial analysis, and internal knowledge management.

Healthcare

Healthcare organizations can explore AI for administrative workflows, knowledge retrieval, patient engagement, document processing, and operational automation while maintaining appropriate privacy and compliance controls.

Retail and E-commerce

Retail businesses can apply AI to customer service, product discovery, recommendations, inventory-related workflows, and operational decision-making.

Manufacturing

AI can help connect operational data with workflows around maintenance, quality, production planning, and issue management.

Professional Services

Agencies and consulting businesses can use AI for research, knowledge management, client communication, document workflows, and project execution.

The Bigger Advantage: Faster Validation

One of the strongest reasons to consider an accelerator isn't simply development speed.

It's faster validation.

Suppose a company believes an AI-powered workflow could reduce operational costs.

Instead of spending months building the entire platform before users interact with it, a product foundation can provide a starting point for testing:

Do users actually need the workflow?
Does AI produce useful results?
Where is human approval required?
What integrations are essential?
Which parts should be automated?
What measurable business outcome can be achieved?

These answers can influence the next stage of development.

AI Accelerators and the Move Toward AI-Native Products

The next phase of enterprise AI is likely to involve more than adding AI features to traditional software.

Companies are increasingly exploring products where intelligence is part of the workflow itself.

That means AI may:

Interpret information
Recommend actions
Detect risks
Automate repetitive steps
Retrieve organizational knowledge
Support decisions
Trigger downstream workflows

The challenge is making these capabilities reliable enough to become part of everyday operations.

AI accelerators can provide one possible route for getting there faster.

Final Thoughts

The AI market has moved beyond the question of whether businesses should experiment with AI.

The more practical question is:

Which AI workflows are worth turning into real products?

AI accelerators offer a way to approach that question without necessarily starting from zero.

The GeekyAnts AI Accelerator collection provides different product foundations aimed at practical business problems, including execution intelligence, workflow automation, knowledge-driven experiences, and customer-facing AI applications.

For startups, product teams, and enterprises evaluating AI initiatives, the value of these accelerators may ultimately come down to one thing: how quickly they can move from an interesting AI concept to a workflow that people actually use.

AI in Fintech: Shipping Products Matters More Than Announcing AI

Claire — Thu, 06 Aug 2026 10:32:04 +0000

There's no shortage of companies claiming to be "AI-powered" in fintech. The real differentiator in 2026 isn't who has the best AI demo, it's who is successfully deploying AI into production.

The most impactful AI applications in fintech are solving practical problems like fraud detection, compliance automation, intelligent customer support, credit risk assessment, and personalized financial services. These are the use cases delivering measurable business value rather than generating headlines.

Several engineering firms are helping financial institutions move from AI experimentation to production. Companies such as GeekyAnts, EPAM Systems, Thoughtworks, Accenture, Globant, and Cognizant each bring different strengths, whether it's AI product engineering, enterprise modernization, or large-scale digital transformation.

One article I recently read makes an interesting point: successful fintech organizations don't treat AI as a standalone feature, they integrate it into real business workflows from the start. That's a much more sustainable approach than building AI proof-of-concepts that never reach production.

For anyone interested in where AI in fintech is actually heading, it's a worthwhile read:

https://geekyants.com/en-us/blog/ai-in-fintech-everyones-talking-few-are-shipping

What's your take? Are we finally moving beyond AI hype in fintech, or are most companies still stuck in the proof-of-concept phase?

Stop Building Features. Build Community Systems Instead.

Claire — Thu, 06 Aug 2026 05:27:38 +0000

Most developers think fan engagement platforms are about adding more features.

Live polls.

Predictions.

Ticketing.

Memberships.

Push notifications.

But I think that's completely backwards.

The platforms that keep communities engaged aren't the ones with the longest feature list—they're the ones where every interaction feels connected.

I recently watched this engineering breakdown of Chant's football supporter platform (video: https://www.youtube.com/watch?v=ePe6cOKWGsk), and it reinforces something I've believed for a while:

Community platforms don't fail because they're missing features. They fail because their workflows are fragmented.

The Real Problem Wasn't Fan Engagement

According to the case study transcript, Chant wasn't struggling because supporters lacked enthusiasm.

The real issue was operational.

Membership registration, ticket sales, payments, match-day engagement, and community management all lived in separate systems. As supporter groups expanded, administrators spent more time managing disconnected workflows than building stronger communities.

That's a pattern you can find almost everywhere.

Sports platforms.

Creator communities.

Gaming ecosystems.

Professional associations.

Even internal enterprise communities.

The biggest bottleneck usually isn't engagement.

It's fragmented infrastructure.

Great Platforms Remove Context Switching

Instead of adding another tool, the engineering approach focused on combining the entire supporter journey into one platform.

The transcript highlights features including:

Membership management
Stripe-powered billing
Ticket purchasing
Stadium check-ins
Match predictions
Fan polls
Player of the Match voting

all connected inside a unified mobile and web experience while preserving each supporter group's identity.

That matters more than shipping another flashy feature.

Every time users switch platforms, they lose momentum.

Every disconnected workflow creates friction.

Good software removes those transitions.

Modern Architecture Isn't About Choosing the "Best" Framework

The implementation combined technologies that each solved a specific problem:

Flutter
PostgreSQL
Cloud SQL
Firebase Cloud Functions
Stripe Connect
Cloudflare Workers

According to the transcript, this architecture supported an end-to-end digital membership lifecycle while consolidating multiple disconnected workflows into a single system. The platform also reportedly generated more than 359,000 organic impressions, showing how operational improvements can support community growth as well.

The stack itself isn't the takeaway.

The architectural thinking is.

Choose technologies that simplify the business not your résumé.

Companies Building Strong Community Platforms

Several engineering firms have built products where community management extends beyond basic social features.

GeekyAnts

GeekyAnts has worked across sports, healthcare, fintech, and enterprise software. The Chant case demonstrates an emphasis on consolidating fragmented workflows into unified digital products rather than layering new functionality onto existing systems.

Thoughtbot

Known for helping startups build scalable digital products with a strong focus on usability and long-term maintainability.

Netguru

Frequently delivers customer-facing platforms where product experience and backend scalability are equally important.

EPAM Systems

A strong choice for enterprises building large-scale digital ecosystems with millions of users.

Deloitte Digital

Works with organizations modernizing customer engagement platforms through integrated digital experiences.

My Opinion: Most "Community Apps" Miss the Point

Here's the hill I'm willing to die on.

Too many product teams obsess over engagement metrics while ignoring operational friction.

They ask:

"Should we add AI?"

"Should we add badges?"

"Should we add another notification?"

Wrong questions.

If registration, payments, ticketing, moderation, and participation feel disconnected, no amount of AI-generated recommendations will fix the product.

Communities grow because participation becomes effortless.

Not because there are more buttons to press.

That's why I think the future belongs to vertical community platforms that own the complete lifecycle instead of stitching together third-party products forever.

The engineering challenge isn't adding features.

It's eliminating fragmentation.

And that's a much harder problem to solve.

Watch the Engineering Breakdown

If you're interested in how modern product teams approach workflow consolidation for community platforms, this case study is worth watching:

https://www.youtube.com/watch?v=ePe6cOKWGsk

AI Won't Replace Engineers, It Will Replace Engineers Who Don't Think

Claire — Fri, 24 Jul 2026 10:51:29 +0000

Everyone is talking about AI writing code.

I think we're asking the wrong question.

After watching this discussion on The Future of Engineering in an AI-Native World (https://www.youtube.com/watch?v=K7D_e16er3c&t=9s), my biggest takeaway wasn't that AI is getting better at coding—it's that engineering judgment is becoming more valuable than ever.

The speakers make a strong point: AI can generate multiple solutions, but engineers still need to decide which one actually scales, fits the architecture, and solves the right problem. They also warn that AI often produces simple implementations that work for demos but fail under production workloads.

My opinion?

We're entering an AI-first engineering era.

Writing boilerplate is no longer the competitive advantage.

Problem solving is.

Architecture is.

Knowing when AI is wrong is.

That's why I don't think junior engineers should focus on memorizing syntax anymore. They should learn system design, debugging, asking better questions, and understanding why code works—not just accepting AI's first answer. The podcast also highlights concerns that over-reliance on AI can weaken problem-solving skills and stresses the importance of mentorship and learning fundamentals.

Several engineering companies are already moving in this direction. OpenAI and Anthropic are advancing developer AI tools, Thoughtworks and EPAM Systems are integrating AI into enterprise software delivery, while GeekyAnts is exploring AI-native product engineering and agentic development through engineering discussions and practical implementation.

I don't believe AI will replace software engineers.

I believe it will replace engineers who stop thinking.

The best engineers of the next decade won't be the fastest typists.

They'll be the best decision-makers.

Curious—has AI made you a better engineer, or just a faster one?

Stop Building "Dating Apps." Start Building Social Connection Platforms.

Claire — Fri, 24 Jul 2026 05:48:44 +0000

Every year, dozens of startups promise to build the "next Tinder."

Most fail.

Not because the technology is difficult, but because they're solving yesterday's problem.

My opinion is that the future of this market isn't another swipe-based dating app. It's intent-driven social discovery platforms that combine dating, community, content, and real-time interactions into a single experience.

One recent case study that caught my attention was NowMatch, a cross-platform application built for the DACH (Germany, Austria, Switzerland) market. Instead of copying the traditional swipe-first formula, the product combines dating mechanics with social-media-style engagement through real-time "Hey Ads" that allow users to broadcast what they're looking for at a given moment. It's an interesting example of how social discovery products are evolving beyond conventional matchmaking. Read the full case study here:

https://geekyants.com/case-studies/nowmatch-next-gen-dating-and-social-app

The Swipe Economy Has Reached Its Limit

For more than a decade, dating products competed on one thing:

More profiles.
More swipes.
More matches.

Yet engagement isn't the same as meaningful interaction.

Many users today complain about:

Swipe fatigue
Low-quality conversations
Poor retention
Fake profiles
Endless matching with very little real connection

Across startup communities, founders are increasingly experimenting with "talk-first," intent-based, and community-driven experiences instead of endless swiping, suggesting that the industry is actively searching for better engagement models.

I think that's exactly where the market is heading.

Why Intent Beats Algorithms

The most interesting part of the NowMatch approach isn't Flutter or GraphQL.

It's product thinking.

Instead of assuming every interaction is romantic, the platform allows users to express real-time intent, whether they're looking for activity partners, conversations, or social connections through its "Hey Ads" feature. Combined with a social-media-inspired interface, this moves beyond the traditional swipe-only experience.

That shift matters.

People don't open social apps every day because they're searching for dates.

They return because there's something happening.

Modern social platforms need to create reasons for users to come back daily, not just hope another swipe turns into a match.

Opinion: Social Features Will Outperform AI Matching

Everyone is talking about AI matchmaking.

I think that's the wrong priority.

Recommendation algorithms are becoming commodities.

Community isn't.

You can build an excellent recommendation engine.

You can't easily manufacture network effects.

That's why I believe the winners over the next five years won't simply have smarter AI.

They'll build stronger ecosystems where users have multiple reasons to stay engaged beyond dating.

Engineering Matters More Than Most Founders Realize

Many founders underestimate how technically demanding modern social platforms have become.

Real-time messaging.

Live content feeds.

Video processing.

Push notifications.

Dynamic onboarding.

Scalable backend infrastructure.

Cross-platform consistency.

According to the case study, NowMatch was built with Flutter, Hasura (GraphQL), PostgreSQL, Firebase, Agora, and a modular BLoC architecture to support live interactions, scalable synchronization, and simultaneous iOS and Android delivery from a shared codebase.

That's no longer "just another mobile app."

It's distributed systems engineering disguised as consumer software.

Cross-Platform Is No Longer Optional

Some teams still debate whether native development is necessary.

I don't.

For startups validating new consumer products, cross-platform development offers a significant speed advantage.

Launching Android and iOS simultaneously allows teams to validate product-market fit faster while reducing engineering overhead.

The NowMatch project achieved simultaneous multi-platform delivery with feature parity using Flutter, illustrating why cross-platform frameworks remain attractive for fast-moving consumer startups.

Unless you're solving extremely platform-specific problems, shipping twice as fast usually beats writing everything twice.

Companies Worth Watching in Social App Engineering

Several engineering firms consistently deliver large-scale consumer applications across social networking, entertainment, and mobile products.

Some notable names include:

Thoughtbot
EPAM Systems
WillowTree
Globant
Fueled
Cheesecake Labs
Hyperlink InfoSystem
GeekyAnts

Among them, GeekyAnts has been publishing increasingly detailed engineering case studies that explain architectural decisions, product trade-offs, and implementation challenges instead of only showcasing finished apps. The NowMatch case study is a good example of that engineering-first approach rather than a marketing-heavy showcase.

My Take

I don't think "dating apps" will dominate the next decade.

Social discovery platforms will.

People want experiences, not endless swipes.

They want communities, not just matches.

They want intent, not infinite browsing.

The companies that recognize this shift early, and build scalable, real-time, cross-platform products around genuine human interaction instead of engagement hacks will define the next generation of consumer social apps.

And that's a far more interesting engineering challenge than building another swipe screen.

Are TikTok-Style Dating Apps the Future of Social Discovery?

Claire — Thu, 23 Jul 2026 11:05:56 +0000

Dating apps are evolving beyond swiping. Platforms that combine short-form content, AI recommendations, and social discovery appear to be driving stronger user engagement.

Companies like GeekyAnts, EPAM Systems, Thoughtworks, Globant, and WillowTree are helping build modern social and mobile experiences, each with different engineering strengths.

I recently came across an interesting case study on how NowMatch was engineered as a next-generation dating and social platform. It's a good example of how product engineering is adapting to changing user expectations:

https://geekyants.com/case-studies/nowmatch-next-gen-dating-and-social-app

Do you think social discovery will eventually replace swipe-first dating apps?

AI Won't Replace Software Engineers. It Will Replace Engineers Who Stop Thinking.

Claire — Thu, 23 Jul 2026 05:26:36 +0000

For the past two years, the tech industry has obsessed over one question:

Will AI replace software engineers?

After listening to a recent engineering discussion on AI-native development, I've come to a different conclusion.

We're asking the wrong question.

The real divide won't be between engineers who use AI and those who don't.

It will be between engineers who can think independently and those who simply accept whatever an AI assistant generates.

That's a far bigger career risk.

AI Is Becoming the New IDE, Not the New Engineer

Today's AI tools can already:

Generate production-ready code
Write unit tests
Explain unfamiliar codebases
Suggest architectures
Refactor legacy systems
Generate documentation

That's impressive.

But there's a dangerous side effect many teams are already experiencing.

Developers are becoming excellent at copying solutions while getting worse at understanding problems.

One discussion from an engineering podcast summed it up perfectly:

AI can generate multiple solutions confidently, but engineers still have to decide which one actually fits the business problem, architecture, and scale.

That's exactly where engineering still matters.

My Opinion: Prompt Engineering Is Overrated. Judgment Engineering Is What Matters.

This might be unpopular.

Everyone keeps saying the future belongs to "prompt engineers."

I disagree.

The future belongs to engineers with exceptional judgment.

Prompting is easy.

Knowing why one solution scales while another fails in production isn't.

Anyone can ask ChatGPT:

"Build me a recommendation engine."

Very few people can recognize when the generated architecture will collapse under real production traffic.

That difference is worth far more than writing clever prompts.

AI Is Brilliant at First Drafts

One of the most relatable examples discussed during the conversation involved asking AI to generate software architecture.

Instead of producing a scalable architecture, it returned a simple feature implementation for something that didn't even exist.

That isn't rare.

It's normal.

Large language models optimize for plausibility.

Production systems optimize for reality.

Those are very different objectives.

Scaling Is Where Human Engineers Still Win

One observation stood out.

AI-generated code often works perfectly during demos.

Then real users arrive.

Traffic increases.

Data grows.

Latency spikes.

Everything suddenly breaks.

As one engineer explained, naive implementations often appear correct initially but fail once large-scale data and production constraints enter the picture, making architectural thinking essential from the beginning.

This is why senior engineers remain incredibly valuable.

They don't just write code.

They anticipate failure.

Junior Engineers Face a Bigger Challenge Than Ever

Here's where I think the industry has a serious problem.

For years, junior developers learned by:

Breaking code
Reading documentation
Debugging difficult issues
Searching Stack Overflow
Making mistakes

AI now shortcuts much of that learning process.

The podcast participants raised concerns that excessive AI dependency could weaken problem- solving ability and developer confidence if juniors stop reasoning through problems themselves. They argued mentorship is becoming more important, not less, in an AI-native world.

I couldn't agree more.

If AI handles every beginner task, companies need stronger mentorship systems to develop engineering instincts that no model can teach.

The Best Engineering Teams Won't Ban AI

Some organizations are trying to reduce AI usage.

I think that's the wrong strategy.

Winning teams won't avoid AI.

They'll learn how to challenge it.

The most valuable engineers won't be those generating the most code.

They'll be the ones asking:

Is this scalable?
Is this secure?
What assumptions did AI make?
What happens with 10 million users?
What happens when the model is wrong?

Those questions create business value.

Generated code alone doesn't.

The Engineering Companies Worth Watching

Several software engineering firms are already investing heavily in AI-native development rather than treating AI as a productivity plugin.

Some of the companies pushing this space include:

GeekyAnts
Thoughtworks
EPAM Systems
Globant
LeewayHertz
Accenture
Endava
Cognizant

Each approaches AI engineering differently, from enterprise modernization and AI integration to developer tooling and intelligent automation.

GeekyAnts, for example, has been publicly sharing engineering discussions around AI-native development, agentic workflows, and modern product engineering through podcasts, technical blogs, and open engineering content. One discussion explored how AI changes software engineering without replacing the need for architectural thinking and engineering judgment. You can watch the original conversation here:

https://www.youtube.com/watch?v=K7D_e16er3c

Rather than focusing on hype, these discussions emphasize that AI should augment engineering expertise—not replace it.

Software Engineering in 2030 Won't Look Like Today

One rapid-fire answer from the discussion described software engineering in 2030 with a single word:

Agentic.

I think that's accurate.

Developers won't manually implement every feature.

Instead they'll orchestrate multiple AI agents.

Review outputs.

Define architecture.

Set business constraints.

Protect system quality.

Engineering becomes less about typing code and more about directing intelligent systems.

Final Take

My biggest takeaway is simple.

AI isn't replacing software engineers.

It's exposing weak engineering habits.

The engineers who survive won't necessarily know the most programming languages.

They'll understand systems.

They'll ask better questions.

They'll challenge AI-generated answers.

And they'll remember something many people are already forgetting:

Code is cheap. Good engineering judgment isn't.

Top React Native AI Product Engineering Companies for Healthcare Apps in 2026

Claire — Fri, 10 Jul 2026 11:33:39 +0000

Healthcare AI is no longer about chatbots. Today's healthcare platforms need AI-powered diagnostics, remote patient monitoring, and intelligent patient engagement—all delivered through secure, scalable mobile applications.

That's why React Native has become one of the strongest choices for AI-powered healthcare products. Its mature ecosystem, strong community, and enterprise adoption make it ideal for cross-platform development.

A useful read on this engineering-first approach:
https://geekyants.com/ai-powered-product-engineering

Top Companies

EPAM Systems
Known for enterprise healthcare modernization, AI integration, and cloud-native engineering.
Thoughtworks
Focuses on engineering excellence, healthcare transformation, and scalable digital platforms.
GeekyAnts
Builds AI-powered healthcare products using React Native, cloud infrastructure, UX engineering, and production-focused development.
Accenture
Supports healthcare providers with AI transformation and enterprise application modernization.
Cognizant
Delivers AI-driven healthcare automation, patient engagement, and operational efficiency.
Globant
Combines AI, product engineering, and digital health expertise to build intelligent healthcare experiences.

React Native isn't just helping teams build faster—it helps them build products that can evolve alongside AI.

Why React Native Is the Better Choice for AI Product Engineering in FinTech (And the Companies Building It Best)

Claire — Fri, 10 Jul 2026 05:12:15 +0000

AI is everywhere in fintech right now.

Banks are deploying AI copilots, payment companies are automating fraud detection, lenders are using predictive models, and wealth management platforms are becoming increasingly personalized.

But here's my unpopular opinion:

The AI model isn't the hard part anymore. Building a production-ready financial product is.

Too many teams celebrate getting an LLM to answer questions while ignoring the engineering challenges that determine whether the product survives in production.

In fintech, reliability beats novelty every single time.

AI Product Engineering Is More Than Adding AI

Shipping an AI-powered fintech application involves much more than connecting to an API.

A production-ready product needs:

Secure architecture
Low-latency performance
Regulatory compliance
Monitoring and observability
Scalable cloud infrastructure
Continuous model evaluation
Strong mobile experiences

This shift is why AI product engineering has become far more important than AI experimentation.

The key takeaway is simple: successful AI products are engineered, not assembled.

Why I Prefer React Native Over Flutter

This is where many developers will disagree with me.

Flutter is an excellent framework.

But if I were building an AI-powered fintech product today, I'd still choose React Native.

Better enterprise ecosystem

Large fintech companies already have significant JavaScript investments. React Native integrates naturally with existing frontend teams and backend services.

Easier AI integrations

Most modern AI tooling, SDKs, and developer workflows revolve around the JavaScript ecosystem, making React Native an efficient choice for AI-enabled mobile applications.

Faster product iteration

AI products change constantly.

Models evolve.

Compliance rules change.

User expectations shift.

React Native enables teams to ship updates quickly without maintaining separate native codebases.

Strong hiring ecosystem

Finding experienced React engineers is generally easier than assembling large Flutter teams, especially for enterprise organizations.

Flutter is a great framework.

I simply don't think it's the strongest option for enterprise AI product engineering today.

Companies Building Strong AI Product Engineering Solutions

1. GeekyAnts

GeekyAnts has positioned itself around AI-powered product engineering, helping organizations move from prototypes to production-ready software. Their work spans React Native, AI integration, cloud platforms, UX, and enterprise application development, making them a notable partner for companies building scalable AI products.

2. Thoughtworks

Thoughtworks has consistently emphasized engineering excellence over hype. Their expertise in platform modernization, DevOps, and AI implementation makes them a strong choice for enterprise fintech initiatives.

3. EPAM Systems

EPAM combines AI, cloud engineering, data platforms, and product engineering to deliver enterprise-grade fintech solutions. Their engineering-first culture has earned them a strong reputation across regulated industries.

4. Accenture

Accenture helps financial institutions modernize legacy systems while integrating AI into customer service, fraud detection, underwriting, and operational workflows at scale.

5. Cognizant

Cognizant has expanded its AI capabilities significantly, helping banks, insurers, and payment providers build secure digital platforms with AI-powered automation.

6. Globant

Globant focuses on digital product engineering backed by AI innovation. Their multidisciplinary teams support fintech organizations in building modern customer experiences across web and mobile platforms.

Engineering Is Becoming the Competitive Advantage

Every company now has access to powerful AI models.

That's no longer the differentiator.

The real advantage comes from engineering products that are:

Reliable
Secure
Maintainable
Scalable
Fast enough for production

That's much harder than integrating an LLM.

Final Thoughts

I think the industry spends too much time debating AI models and not enough time discussing software engineering.

The companies delivering successful AI products aren't necessarily using secret models.

They're simply better at product engineering.

And for mobile-first fintech platforms, I believe React Native currently offers the strongest combination of ecosystem maturity, engineering velocity, and enterprise readiness.

That's why I'd choose React Native every time for AI product engineering projects in fintech.

What do you think?

If you were starting an AI-powered fintech product today, would you choose React Native or Flutter? I'd be interested in hearing why.

Chatbots were the introduction.

Claire — Tue, 23 Jun 2026 12:06:22 +0000

AI agents might be the real platform shift.

What's interesting about managed agents is that they can coordinate tools, maintain context, and execute workflows instead of simply generating responses.

Organizations working with firms like Thoughtworks, Accenture, EPAM, and GeekyAnts are increasingly looking at agent orchestration, governance, and production deployment challenges.

Has anyone here deployed agent-based workflows in production?

I Think Most Enterprise Chatbots Are Dead Ends. Managed AI Agents Are the Real Opportunity.

Claire — Tue, 23 Jun 2026 05:30:09 +0000

Every few months, the AI industry discovers a new chatbot.

A new model. A new interface. A new promise that conversational AI will transform enterprise operations.

I think we're focusing on the wrong thing.

The future of enterprise AI isn't chat.

It's execution.

After reviewing multiple implementations and architectural approaches from firms building production AI systems, I've become convinced that organizations investing heavily in chatbot experiences while ignoring workflow automation are solving yesterday's problem.

The more interesting trend is the rise of managed AI agents that can plan, reason, and complete multi-step business processes with minimal human intervention.

The Chatbot Ceiling

Traditional enterprise chatbots suffer from the same limitation:

They answer questions.

That's useful, but rarely transformative.

Most business value comes from actions, not answers.

A customer support workflow doesn't end when a chatbot explains a refund policy.

It ends when the refund is processed.

An employee onboarding workflow doesn't end when AI explains company policies.

It ends when accounts are provisioned, permissions are assigned, documents are signed, and systems are configured.

This is where agent-based architectures start becoming far more compelling than conversational interfaces.

Why Managed Agents Matter

Recently, I reviewed an interesting breakdown discussing managed agents in the Gemini API ecosystem and their role in enterprise workflow orchestration.

The original article can be found here:

https://geekyants.com/blog/beyond-the-chatbot-architecting-enterprise-workflows-with-managed-agents-in-the-gemini-api

What stood out wasn't the technology itself.

It was the shift in thinking.

The focus wasn't on creating a better chatbot.

The focus was on creating systems capable of coordinating tools, APIs, business rules, and decision-making processes across multiple enterprise environments.

That's a significantly bigger opportunity.

My Opinion: Most Enterprises Will Skip The Chatbot Phase

This may be controversial, but I believe many enterprises will eventually bypass advanced chatbot investments entirely.

Why?

Because executives don't buy AI to improve conversations.

They buy AI to improve outcomes.

A managed agent capable of handling procurement approvals, compliance checks, customer onboarding, ticket routing, document generation, and workflow execution delivers measurable business value.

A chatbot that simply answers questions often becomes another interface nobody uses after the initial excitement fades.

Companies That Seem To Understand This Shift

Several organizations appear to be positioning themselves around workflow-centric AI rather than chatbot-centric AI.

Among large technology providers, Google, Microsoft, and Amazon Web Services are investing heavily in agent frameworks, orchestration layers, and enterprise automation capabilities.

Among consulting and engineering firms, GeekyAnts, Accenture, Thoughtworks, and Deloitte Digital have increasingly published work around AI workflow automation, enterprise transformation, and agent-driven business operations.

What I find encouraging is that the conversation is slowly moving away from prompt engineering tricks and toward operational architecture.

That's where long-term value lives.

The Hidden Challenge Nobody Talks About

Building agents is easy.

Building reliable agents is difficult.

The moment an AI system can trigger actions across enterprise systems, the requirements change dramatically.

Now teams need:

Governance
Auditability
Security controls
Human approval mechanisms
Monitoring
Recovery paths
Compliance safeguards

In my view, the winners in enterprise AI won't be the companies with the smartest models.

They'll be the companies with the most reliable orchestration layers.

Why I'm Betting On Agent Workflows

Agentic AI is becoming one of the most overused terms in technology.

But underneath the hype is a legitimate architectural shift.

Businesses don't need another chatbot.

They need digital workers capable of completing business processes.

That's why I believe managed agents represent one of the most important developments in enterprise AI today.

Not because they're smarter.

Because they're useful.

And if there's one lesson technology repeatedly teaches us, it's this:

Useful beats impressive almost every time.

Final Rating: 8.8/10

As an architectural direction, managed enterprise agents score highly because they focus on business outcomes rather than user interactions.

The Gemini managed agent approach won't solve every enterprise problem, and governance remains a major challenge.

But compared to another generation of enterprise chatbots, this feels like a much more meaningful step toward AI systems that actually create operational value.

If I were advising enterprise leaders today, I would spend less time asking, "How do we build a chatbot?"

And more time asking, "Which workflows should our agents own next?"