DEV Community: Sahil Khurana

Is .NET Right for Your Business? An Honest Answer for Non-Technical Leaders

Sahil Khurana — Mon, 25 May 2026 13:30:00 +0000

A few months back I was on a call with a founder — healthcare SaaS, serious product, smart team — who admitted something that I've heard in different forms from a lot of people in his position. "I nodded through the whole architecture conversation", he said., "I had no idea what they were actually recommending or why". That's more common than anyone likes to admit. Executives and business owners sit through technology discussions constantly, make consequential decisions based on them, and often have less context than they let on. Not because they're not sharp — but because most technology writing is aimed at people who already know the vocabulary.

This is my attempt at something different. No assumed background. No jargon left unexplained. Just a straight answer to the question a lot of business owners are quietly sitting with: is .NET actually the right foundation for what I'm trying to build?

What .NET is, without the technical theater

Here's the plainest version I can give you.
.NET is a free, open-source platform that developers use to build software. Web applications, mobile apps, desktop tools, cloud systems — it handles all of them from a single foundation. Microsoft built it, but it's been open source for years, which matters because it means a massive global community of developers contributes to it, maintains it, and builds tools on top of it.

It's been around long enough to be genuinely battle-tested. Not "we launched this two years ago and it's going great" battle-tested. Decades of enterprise use, including at some of the largest companies in the world, battle-tested. The bugs that would have sunk it already got found and fixed. The edge cases that only show up at scale — someone's already hit them.

The way I usually describe it to non-technical leaders: think of it less like a single tool and more like a fully stocked workshop. When a development team chooses .NET, they're not starting from a blank room and building their own hammers. The foundational work is already there. They're building your product, not rebuilding infrastructure that already exists.

Why businesses actually choose it — in plain terms

It doesn't fall apart when you grow.
A lot of platforms work beautifully at a few hundred users and quietly start struggling at fifty thousand. .NET is built for the long game. The architecture handles serious scale — millions of concurrent users — without requiring your team to re-engineer the foundations they already built.

Why this matters for your business: rebuilding a platform you've already launched is one of the most expensive, operationally disruptive things a company can go through. It's not just the development cost — it's the downtime risk, the migration complexity, the trust you're asking customers to extend while you're in the middle of it. Getting the foundation right the first time is genuinely cheaper than fixing it later, even if it doesn't feel that way at the beginning.

Security is part of the structure, not a layer you add.
Businesses handling sensitive data — financial records, patient information, private customer data — can't treat security as something they'll figure out once the product is live. The breach usually happens before that conversation happens.

.NET builds security controls directly into the framework. Access management, authentication, permission structures — these aren't afterthoughts your team configures after launch. They're part of how the platform is designed. For regulated industries especially, this changes what compliance looks like in practice.

Your team ships faster, which is directly cheaper.
The biggest cost in software development is engineering time. .NET comes with an enormous library of pre-written, pre-tested code that developers build on rather than recreate from scratch. Less time writing boilerplate, fewer bugs from code nobody's battle-tested before, faster delivery on features that actually matter to the business.

"Mature platform" sounds boring. The financial implication isn't — it means you're not paying your engineers to rediscover problems that were already solved.

You're not locked into one infrastructure decision.
With .NET Core, your application runs on Windows, macOS, or Linux. That means your hosting choices aren't dictated by the technology, your team can work across different machines, and if your infrastructure strategy changes in two years you're not rebuilding to accommodate it. You own the decision.

What actually gets built on it

Custom enterprise applications — the internal tools that run operations. Approval workflows, reporting systems, platforms built around how your team actually works rather than how someone else's team worked.

E-commerce platforms built for real traffic and real transaction volume — the kind that doesn't fall over on your highest-volume day of the year.

Financial and accounting systems where accuracy isn't optional and the cost of a bug extends beyond embarrassing into potentially catastrophic.

CRM and ERP solutions that tie together customer data, operations, and resources into something teams actually use rather than work around.

SaaS products built to acquire users, retain them, and scale as the business grows — subscription software where the architecture has to support the business model from the start.

If what you're building needs to last more than 18 months, handle genuine user growth, and not become a liability — .NET belongs on the shortlist.

The part that actually determines whether this goes well

Here's what I've observed over years of watching software projects succeed and fail: the technology choice is rarely the deciding factor. The partner choice usually is.

A good .NET development company isn't just a group of people who know the framework. They bring discipline to project management, they communicate clearly, and — most importantly — they can translate between what your business actually needs and what the technology can realistically do.

That translation layer is where most in-house teams struggle and where a good external partner earns the fee.

When you're talking to potential partners, a few things tell you more than any sales conversation:

Ask to see work they've shipped that's similar to what you're building. A portfolio of actual projects is more revealing than any pitch deck. Ask about the challenges that came up and how they handled them — not just the wins.

Pay attention to how they communicate before the contract is signed. If you're already chasing someone for responses or getting vague answers to direct questions, that pattern doesn't improve once they have your money.

Ask specifically what happens after launch. Software isn't a one-time purchase. It needs maintenance, updates, and someone who understands what was built when something breaks eight months from now. Get the post-launch support structure in writing and make sure it's specific.

Ask how they handle scope changes. Something will change mid-project. The question is whether your partner has a process for handling that cleanly or whether it becomes a point of friction every time.

On outsourcing: when it makes sense and when it doesn't

For a lot of businesses, outsourcing .NET development is genuinely the right call. Building an in-house engineering team is slow — recruiting takes months, salaries are significant, and finding specialists with specific expertise in your local market isn't always realistic.

Outsourcing gives you access to that depth without the overhead.
Good outsourcing partners offer flexible engagement structures — you can scale the team up for a major build and pull back once you're in maintenance mode. That flexibility is hard to replicate with full-time headcount.

The caveat that's worth saying directly: outsourcing only works well when you choose the right partner and set clear expectations from the beginning. A low-cost vendor with no accountability structure isn't cheaper — it costs more in rework, delays, and eventually in rebuilding things that weren't built correctly the first time. I've seen this happen enough times that it's worth being blunt about it.

Four questions worth asking before any code gets written

You don't need a technical background to ask these. The answers will tell you most of what you need to know about whether a project is set up to succeed.

What exactly are we building, and where does it need to run?
Web, mobile, cloud, some combination — the answer shapes everything else about the project scope and timeline.

What's the realistic timeline, and how does scope get handled? If you get dramatically different answers from different vendors, that's important information. Either the scope isn't defined clearly enough or someone is telling you what you want to hear.

How does this stay secure and scalable as we grow? Any serious development partner should answer this without hesitation or vagueness. If you're getting generalities, push for specifics.

What does support look like after we go live? This is the question that reveals the most about a vendor's actual intentions. Get specifics. Vague answers about "ongoing support" aren't commitments.

The honest answer to the original question

If you're building something that needs to last, handle real user growth, stay secure across regulated or sensitive data environments, and work across different infrastructure setups — .NET is a genuinely strong choice. Not because it's trendy. Because it's proven over a long enough timeline that the risk profile is well understood.

The talent pool is deep. The ecosystem is mature. The framework handles the hard parts of scaling and security so your team can focus on building the actual product.

But the technology is one piece. The more consequential piece is finding a development partner who can take that foundation and build something that serves your business — not just something that technically ships.

If that decision is in front of you, it's worth talking to people who've navigated it before.

At Innostax, we've helped businesses make this exact call — evaluating .NET as the right foundation and finding the right partner to build on it. If you're trying to figure out whether .NET is the right fit for your product, innostax.com/contact is where that conversation happens.

Originally published on the Innostax Engineering Blog.

Building Hyper-Secure .NET Applications with Zero-Trust Security

Sahil Khurana — Mon, 25 May 2026 09:30:00 +0000

I'll start with something that took me longer than I'd like to admit to fully accept.

Most enterprise security architectures I've worked with — or been brought in to review — are built on a quiet assumption nobody made explicitly: traffic inside the network is probably fine. Nobody wrote that assumption down. Nobody approved it in a design review. It just accumulated, decision by decision, until entire systems were sitting on top of it like a house built on sand.

Attackers understood this years before most security teams did. The breaches that followed weren't particularly sophisticated. They were just logical.

So this is what I actually want to talk about — not Zero-Trust as a buzzword, but what it means for .NET developers building applications that sit near things that matter. Critical infrastructure. Healthcare systems. Financial networks. Environments where "we had an incident" has consequences that extend well past a support ticket.

The perimeter model is gone. Most architectures haven't caught up.

Here's the environment most enterprise .NET applications actually run in today: distributed across multiple clouds, accessed from home networks, co-working spaces, personal devices, contractor laptops. VPN tunnels to infrastructure that itself spans regions. Services talking to services across boundaries that nobody fully mapped.

The "trusted internal network" that traditional perimeter security was designed to protect doesn't describe this environment. Treating it as if it does — granting broad access to anything that made it past the front door — is why lateral movement after a compromise is so common. The attacker gets one foothold and then walks.

Zero-Trust rejects the assumption at the root. Not "is this person inside the network?" but "should this specific identity, on this specific device, with this access pattern, be allowed to do this specific thing right now?" Every request. Every time. Not just at login.

Why critical infrastructure specifically

Worth being direct about who needs to care most.

Financial networks, healthcare systems, energy grids, industrial control systems — these aren't just IT assets. They're interconnected with things that affect people in the physical world. A breach in a SCADA network isn't a data loss event. A ransomware hit on a hospital network has patient safety implications.

And the interconnectedness that makes these systems valuable is exactly what makes them hard to defend with perimeter approaches. One compromised vendor credential, one phishing click on a contractor's machine, and you're inside a network that trusted you because you came from the right direction.

Zero-Trust doesn't pretend the perimeter holds. It builds defenses that assume it won't — so when a credential gets compromised (and it will), the blast radius is contained rather than catastrophic.

The four principles, and why skipping any of them is a mistake

Never trust, always verify. Every identity, device, and access request gets validated based on real-time context — not just "did this person log in successfully six hours ago." MFA is table stakes here, not a nice-to-have. Context-aware access controls factor in geolocation, device health, behavioral signals. A login attempt from a location your users never log in from gets treated differently than one from their usual city. This sounds obvious. Most environments don't do it.

Least-privilege access. Users and services get exactly what they need for their specific job. Nothing extra. In practice, most environments I've seen have accumulated permissions that far exceed what anyone actually needs — because it was easier to say yes at the time than to think carefully about what "yes" actually unlocked. Fine-grained RBAC fixes this. A compromised account with narrow permissions is a contained problem. A compromised account with broad access is a disaster.

Continuous monitoring and analytics. Zero-Trust is built on an honest premise: breaches happen. The goal isn't zero incidents. It's detecting anomalies fast enough to limit the damage. Real-time behavioral monitoring, not quarterly audits. AI and ML models are increasingly important here — rule-based systems catch what someone already thought to write a rule for. Behavioral models catch the things that don't match any known pattern.

Authentication, authorization, and encryption everywhere. No exemptions for internal traffic. MFA and biometrics at every access point. OAuth 2.0 and OpenID Connect for granular authorization. TLS 1.3 and AES-256 for everything in transit and at rest — including traffic that never leaves your internal network. Internal traffic that skips encryption is a trust assumption. Zero-Trust doesn't make trust assumptions.

What this looks like in actual .NET code

The .NET ecosystem has matured into this well. You're not building from scratch.

Identity and authentication: ASP.NET Core Identity handles the baseline. Azure Active Directory scales that to enterprise level — centralized management across users, apps, and policies, which matters when you have dozens of services and thousands of users. Microsoft Identity Platform sits on top for MFA, conditional access, and risk-based authentication.

Microservices and APIs: Every endpoint is a potential entry point. OAuth 2.0 and OpenID Connect enforce token-based access across service-to-service communication. Azure API Management as a central gateway means security policies apply uniformly — the alternative is each team implementing them inconsistently, which is how gaps appear.

The development pipeline: Security that starts at deployment is already behind. HTTPS enforcement at the configuration level via dotnet CLI. OWASP scanning integrated into CI/CD. Vulnerabilities caught during development cost a fraction of what they cost after users find them.

The seven areas that actually need covering

Secure coding. XSS and injection defenses built into development standards from day one, not added as an afterthought. Veracode or equivalent SAST running in CI/CD — vulnerabilities found before they ship, not after.

API security. Azure API Management with CORS policies enforced. Every external origin that can touch your APIs should be there deliberately, reviewed, documented. Not just "CORS is open because that was the easiest way to get it working."

MFA coverage. Complete. Not just admin accounts. Not "we'll get to the others eventually." All accounts with access to critical systems. Exemptions should be explicit risk decisions, documented and reviewed — not quiet workarounds that became permanent.

End-to-end encryption. HTTPS enforced at the application level, not just at the load balancer. Azure Key Vault for cryptographic keys and secrets. Hardcoded credentials in a codebase aren't a minor code smell — they're a breach waiting to happen and usually a compliance violation on top of it.

Distributed systems security. .NET microservices on AKS with mutual TLS enforced across all service-to-service communication. Internal network traffic doesn't get a pass.

Identity management. Conditional access rules in Azure AD based on role, location, and device health. Privileged Identity Management for elevated permissions — just-in-time approval, not permanent assignment.

Database security. Azure SQL's built-in auditing and encryption, dynamic data masking, row-level security. Users see what their role requires. Not the full dataset because the query technically allowed it.

Three sectors where this plays out differently

Energy. SCADA system access is the critical concern — unauthorized access to industrial control systems can cause physical damage in the real world. IoT edge security matters here too, because .NET control applications talk to devices that often can't be fully hardened themselves.

Healthcare. Patient records, HIPAA, medical devices running older software on the same network as everything else. Continuous monitoring catches anomalous device behavior before it becomes a breach — or a patient safety event. These aren't hypothetical scenarios. They've happened.

Finance. Insider threats are more common in financial environments than anyone likes to discuss openly. Real-time behavioral monitoring, RBAC, and end-to-end encryption across the transaction stack aren't differentiators here — they're baseline expectations from regulators and increasingly from clients.

The friction points worth being honest about

Legacy systems are the hardest part. Applications built before modern authentication protocols existed aren't going to magically support them. Full rewrites aren't realistic for most teams. The practical answer is API gateways and wrappers that enforce Zero-Trust at the boundary — imperfect but far better than leaving legacy systems entirely outside the model.

Scale complexity is real. Authentication and authorization across large distributed systems is hard to manage consistently. Azure AD B2C handles federated identity at scale without custom infrastructure at every service boundary — that's the practical solution, not something you build yourself.

The expertise gap might be the most underestimated problem. Zero-Trust implemented incorrectly doesn't just fail — it creates false confidence that's arguably more dangerous than a security posture you know has gaps. Invest in training before tooling. The tools only work if the team understands what they're configuring.

Tooling worth knowing

Microsoft Defender for Identity — spots compromised identities and unusual behavior before it becomes an incident.

Azure Security Center — central visibility across monitoring, compliance posture, and recommendations.

Azure Key Vault — keeps secrets out of your codebase where they create risk every day they sit there.

ASP.NET Core Identity — authentication and RBAC without building identity infrastructure from scratch.

Microsoft Sentinel — SIEM for threat detection, investigation, and response in one place.

What's actually changing over the next few years

AI in monitoring is moving from rule-based alerts to behavioral models. Rules catch known threats. Models catch anomalies nobody wrote a rule for yet. This shift is already happening and will increasingly shape how dynamic access controls work in practice.

IoT is becoming unavoidable. In energy, healthcare, and manufacturing especially, .NET applications communicate with devices that can't always be secured at the device level. Authenticated connectivity at the edge is moving from an afterthought to a first-class architectural concern.

Quantum computing is a longer horizon — but not so long that planning for it now is premature. Current encryption standards will eventually be vulnerable. Teams that designed for encryption agility early will migrate cleanly when NIST's post-quantum standards are finalized. Teams that didn't will face a different kind of project entirely.

The honest bottom line

If your .NET applications touch anything near critical infrastructure, Zero-Trust isn't something you'll eventually get around to. The longer the implementation waits, the more technical and organizational debt accumulates around security approaches that weren't built for the environment you're actually running in.

What the Microsoft ecosystem gives .NET teams is a solid foundation — Azure AD, Key Vault, Defender, Sentinel — without building a security stack from scratch. The principles are well-established. The tooling is mature.

What it can't do is retroactively protect systems that were designed without it. Security added after the fact costs more, covers less, and tends to miss whatever was already there. Build it in now.

At Innostax, we've helped engineering teams navigate this exact implementation — Zero-Trust architecture, .NET security hardening, and critical infrastructure defense built on the Microsoft ecosystem. If you're trying to figure out the right security foundation for your environment, innostax.com/contact is where that conversation happens.

Originally published on the Innostax Engineering Blog.

Best Programming Language for Backend Web Development: PHP vs Python

Sahil Khurana — Mon, 25 May 2026 05:29:29 +0000

Something I get asked constantly by developers picking up their second or third language: PHP or Python for the backend?

My answer used to be more confident than it should have been. I had a preference, I leaned on it, and I gave advice that was really just personal bias dressed up as guidance. After spending real time in both ecosystems — not just hello-world tutorials, actual production systems — my answer got more complicated and, I think, more useful.

Here's what I actually know.

A bit of context on where each language came from

PHP started in 1994 as literally "Personal Home Page" — a set of CGI scripts Rasmus Lerdorf wrote to track visits to his online resume. That origin story matters more than people realize. PHP was never designed in a university or built as an academic exercise in language theory. It grew organically out of what developers actually needed to put dynamic content on websites. That pragmatism is baked into the language's DNA, for better and sometimes for worse.

Python came from a different place entirely. Guido van Rossum built it as a teaching language with readability as a first principle. He wanted code that read close to plain English. That decision shapes everything about the language — the forced indentation, the clean syntax, the "one obvious way to do it" philosophy. It was never specifically a web language, which is why it ended up everywhere else too.
Neither of these histories makes one language objectively better. But they explain a lot about why each one feels the way it does to work in.

Performance: where it gets more nuanced than most articles admit

The common comparison goes something like: PHP is faster for web requests, Python is faster for computation. That's... mostly true, but it flattens a more interesting reality.

PHP was optimized from the ground up for one thing — serving web responses quickly. Apache, Nginx, FastCGI, PHP-FPM — the whole deployment stack was built around making that pipeline as fast as possible. When you're handling a lot of simple web requests, PHP's performance characteristics are genuinely excellent. WordPress serving millions of requests a day is not a fluke.

Python is slower at basic script execution. That's real and worth acknowledging. But the moment you're doing anything computationally intensive — data processing, numerical operations, anything touching machine learning — the calculus flips. NumPy, TensorFlow, PyTorch — these libraries use compiled C and Fortran under the hood. Python is orchestrating work that's executing at C speed. For that class of problem, Python isn't just competitive, it's dominant.

The practical question isn't which language is faster in the abstract. It's what your application is actually doing most of the time.

Syntax: why this matters more than developers like to admit

I'll say something slightly unpopular: PHP syntax is fine. It's C-like, it's familiar to a huge portion of developers, and it does what it needs to do. The reputation PHP has for messy code is mostly a reputation for messy code that people wrote in PHP — the language doesn't force bad habits, it just doesn't aggressively prevent them either.

Python's syntax is genuinely easier to read, though. The indentation-as-structure thing felt annoying to me for about a week and then clicked. Now reading Python code from someone I've never met feels more legible than reading their PHP, almost without exception. That has real team implications — onboarding, code reviews, debugging at 2am when something's broken. Cognitive overhead is a real cost.

If you're building a team that will include developers at various experience levels, Python's readability is not a minor consideration.

Frameworks: picking the right tool for the actual job

PHP: Laravel is the clear modern choice for most projects. Full-featured, well-documented, actively maintained, with an ecosystem that's grown substantially in the past five years. Symfony is the other serious option — more modular, used heavily in enterprise contexts. CodeIgniter is still around for lighter use cases but I'd be hard-pressed to recommend starting a new project on it today.

Python: Django for anything that needs a full-featured framework with an ORM, admin interface, and authentication baked in. It's opinionated and that's mostly a strength — less time making decisions that don't matter, more time building. Flask when you want something lighter and more explicit. FastAPI is the interesting newer option — async-first, excellent for APIs specifically, and the automatic documentation generation alone saves real time.

The right framework matters more than the language choice in most cases. A well-structured Django project beats a messy Laravel project. The inverse is also true.

Where each one actually wins

There are situations where the choice is clear and pretending otherwise wastes everyone's time.

Go with PHP if:

You're building a content-heavy website or CMS. The WordPress ecosystem is vast and PHP is its native language — fighting that is a choice, not a necessity. You need something deployed quickly on shared hosting with minimal configuration. Your team already knows PHP and the project doesn't justify the switching cost. You're maintaining an existing PHP codebase that's working.

Go with Python if:
Any part of your roadmap involves machine learning, data analysis, or AI features — even if it's just a "we might add this later" item. The libraries available in Python for this work have no real equivalent in PHP. You want language flexibility to use the same stack across web development, scripting, data work, and automation. You're hiring developers and want access to a wider candidate pool that includes data science backgrounds.

The honest hybrid answer: A fair number of production systems I've seen use both. PHP serving the web application, Python handling background jobs, data pipelines, or ML inference. This isn't architectural complexity for its own sake — it's using each language for what it's genuinely better at.

Database and deployment: the parts that feel boring until they cause problems

Both languages have solid database support. PHP's built-in MySQL support has been there since the beginning — it's mature, well-understood, and generally not the source of problems. Python's SQLAlchemy is one of the better ORM abstractions I've worked with, flexible enough to not feel like it's fighting you when you need to write complex queries.

Deployment is where the differences feel more practical. PHP applications fit naturally onto Apache or Nginx with PHP-FPM — the deployment model is old and therefore extremely well-documented. Python web apps need a WSGI or ASGI server (Gunicorn, uWSGI, Uvicorn for async) in front of them, which is a slightly more involved setup. Not complicated, just more pieces to understand.

Container-based deployments smooth out a lot of this difference. If you're running everything in Docker anyway, the gap narrows considerably.

Community: this matters more than it sounds

PHP has a larger web-specific community and has been in production longer. The answers to obscure PHP deployment questions are often on Stack Overflow threads from 2011 — which is either reassuring or terrifying depending on how you look at it. The WordPress developer ecosystem is enormous and PHP-first.

Python's community has expanded well beyond web development into data science, scientific computing, automation, and AI research. This breadth means Python developers often bring cross-domain knowledge that pure web developers don't. It also means the community is split across more use cases, so some web-specific questions can take longer to track down.

Both communities are active. Neither is at risk of going dark. This isn't a deciding factor for most projects, but it's worth knowing what you're joining.

The actual conclusion — not the diplomatic one

If I'm building a content website, a CMS, or a high-traffic web application where the primary complexity is serving lots of requests efficiently: PHP, probably Laravel.

If I'm building anything where data analysis, machine learning, or AI are part of the picture — even in a small way, even as a future consideration: Python, no real competition.

If I'm building a startup product where the technical requirements aren't fully locked in yet and I want maximum flexibility in where the codebase can go: Python. The optionality is worth the slightly steeper initial setup.

If I'm inheriting an existing codebase: I use whatever's already there unless there's a specific compelling reason not to. Switching languages mid-project has costs that are almost always underestimated.

The framework you pick matters as much as the language. The quality of the codebase you build matters more than either. Both PHP and Python can produce excellent backends and terrible ones. Neither choice saves you from having to think carefully.

At Innostax, we build with both — the choice comes from what the project actually needs, not what we happen to prefer. If you're figuring out the right stack for something you're building, innostax.com/contact is the right place to start that conversation.

Originally published on the Innostax Engineering Blog.

Streamlined Customer Service: Chat Integration with Gladly

Sahil Khurana — Fri, 08 May 2026 13:41:29 +0000

Customer expectations have never been higher. Businesses that fail to respond quickly and personally across every channel risk losing customers permanently. Gladly is a modern customer service platform built to solve exactly this problem — and integrating its chat capabilities into your website is one of the most impactful steps you can take toward delivering exceptional support.

What is Gladly?

Gladly organizes every customer interaction in one place. Unlike traditional ticketing systems, it unifies emails, phone calls, text messages, social media, and live chat into a single interface, giving agents a complete view of every customer's history. This makes communication management simple, efficient, and deeply personal. If you're evaluating platforms for your next custom software project, Gladly is worth serious consideration.

Advantages of Gladly

Unified Communication — All customer conversations across every channel flow into one interface. Agents never lose context, no matter how a conversation started. This is the foundation of a great customer experience strategy.
Customer Profiles — Gladly builds rich profiles that include contact details, purchase history, and past inquiries, allowing agents to deliver genuinely personalized assistance every time.
Team Collaboration — Agents can share notes, assign conversations, and work together in real time — eliminating siloed inboxes and missed handoffs.
Automation and AI — Gladly handles repetitive queries automatically, prioritizes important responses, and frees agents to focus on complex issues that need a human touch. Learn more about AI integration in customer workflows.
Analytics and Reporting — Supervisors can track response times, customer satisfaction scores, and agent performance, helping teams spot patterns and continuously improve their operations.

Chat Integration with Gladly

Chat integration with Gladly brings real-time messaging directly into your customer service workflow. The centerpiece is the Glad App — an embeddable widget that lets website visitors search help content or connect with a live agent instantly. This kind of API-driven integration is exactly where experienced development teams add the most value.

The Glad App API gives developers programmatic control over the widget. Add a JavaScript loader snippet to every page of your site — it asynchronously loads the Chat SDK without blocking page performance.

Gladly.init({
  appId: "your-own-id"
}).then(function() {
  // This is called once Glad App has been initialized.

  // An element with the id "start-chat" is assumed to be on the page.
  var startChatButton = document.getElementById('start-chat');

  // An onClick handler can be attached to the "start-chat" element
  startChatButton.onclick = function() {Gladly.show()};

  // This API call gets the current availability
  var availablility = Gladly.getAvailability();

  // Setting the availability as the class name on the "start-chat" element,
  // this allows us to change the look of the button via CSS.
  startChatButton.setAttribute('class', availablility.toLowerCase());

  // Subscribe to changes in availability, and update the class on the "start-chat" element.
  Gladly.on('availability:change', function(availablility) {
    startChatButton.setAttribute('class', availablility.toLowerCase());
  });
}).catch(function(error) {
  // If anything goes wrong when Glad App is being initialized, this gets called.
  console.log('error:', error)
});

Once the loader is in place, initialize the Glad App using your unique appId, found in Settings → Glad App in the Gladly dashboard. For basic use, a simple global config object is all you need.

&lt;script&gt;
 window.gladlyConfig = {
   appId: 'your-own-id'
 };
&lt;/script&gt;

For more control — such as launching chat from your own custom button or adjusting the UI based on agent availability — manual initialization via Gladly.init() gives you full flexibility.

Gladly.init({
  appId: "your-own-id"
}).then(function() {
  // This is called once Glad App has been initialized.

  // An element with the id "start-chat" is assumed to be on the page.
  var startChatButton = document.getElementById('start-chat');

  // An onClick handler can be attached to the "start-chat" element
  startChatButton.onclick = function() {Gladly.show()};

  // This API call gets the current availability
  var availablility = Gladly.getAvailability();

  // Setting the availability as the class name on the "start-chat" element,
  // this allows us to change the look of the button via CSS.
  startChatButton.setAttribute('class', availablility.toLowerCase());

  // Subscribe to changes in availability, and update the class on the "start-chat" element.
  Gladly.on('availability:change', function(availablility) {
    startChatButton.setAttribute('class', availablility.toLowerCase());
  });
}).catch(function(error) {
  // If anything goes wrong when Glad App is being initialized, this gets called.
  console.log('error:', error)
});

The API also provides key methods: setUser() to pre-identify a logged-in customer so they skip the name/email form, getUser() to retrieve the current user, and clearUser() to remove the user on logout. Events like availability:change, message:received, and message:sent let you react to widget activity in real time.

Creating and Configuring the Glad App

Inside the Gladly dashboard, creating a Glad App takes just a few steps — navigate to Settings → Channels → Glad App, click Create Glad App, give it a name, and you're taken straight to the configuration screen.
The screen has three sections: Configuration (business hours, Quick Actions, throttling rules), Style (colors, border radius, widget position), and Text (all copy visible to the visitor). For the full setup walkthrough, refer to Gladly's official documentation.

What is Proactive Chat?

Proactive Chat lets you engage customers before they ask for help. Instead of waiting for a visitor to open the widget, you define campaigns that automatically initiate a conversation based on triggers — pages visited, time on site, cart value, and more. For e-commerce businesses especially, this feature directly impacts conversion rates and cart abandonment.
When setting up a campaign, you define a name, description, priority order, triggers, and an opening greeting. Two optional settings give extra control: Ignore chat throttling bypasses queue limits for high-priority campaigns, and Skip onboarding removes the name/email step to reduce friction. Full campaign configuration details are covered in Gladly's Proactive Chat guide.

Conclusion

Gladly transforms customer service from a reactive process into a proactive, unified experience. With its powerful Glad App API, rich customer profiles, and Proactive Chat campaigns, businesses can deliver faster and more personal support across every channel. If you need help integrating Gladly or building a custom chat solution, Innostax's engineering team is ready to help you ship it right.

About Innostax

Innostax is a custom software development and IT staff augmentation company. We build and integrate customer service platforms, develop custom software solutions, and provide managed engineering teams for startups, scale-ups, and digital agencies — typically in situations where customer experience is broken and needs to be fixed fast.

What Is Cursor AI? The Complete Guide to the AI-Native IDE (2026)

Sahil Khurana — Fri, 08 May 2026 13:24:13 +0000

Cursor AI is a VS Code-based AI code editor with Claude, GPT & Gemini built in.

TL;DR

Cursor AI is an AI-first code editor based on VS Code, built by Anysphere Inc. and co-founded in 2023 by four MIT graduates. Unlike plugins, it runs LLMs like Claude, GPT, and Gemini directly inside the editor — enabling real-time suggestions, autonomous multi-file editing, bug detection, and refactoring without context-switching. Developer teams using Cursor report 20–40% faster feature delivery and 20% fewer production bugs.

Key takeaways

Cursor AI is an AI-native IDE (VS Code fork) with built-in LLM collaboration — not a plugin.
Agent Mode executes multi-file, multi-step tasks autonomously.
Priced from free (Hobby) to $200/month (Ultra); Teams at $40/user/month.
Used by engineers at OpenAI, NVIDIA, Shopify, Coinbase, and 50%+ of Fortune 500.
Honest limitation: requires internet — no offline or air-gapped support.

What is Cursor AI?

Cursor AI is an AI-native integrated development environment (IDE) — an editor built around AI collaboration from the ground up, rather than adding AI as a plugin — based on Visual Studio Code. As opposed to AI coding plugins, which are external to the editors, Cursor is built around AI collaboration, i.e. autocomplete, multi-file editing, agentic task execution and codebase-wide context are part of the editor, rather than extensions of the editors.

Cursor was founded by Michael Truell, Sualeh Asif, Arvid Lunnemark, and Aman Sanger, and is the fastest SaaS company to date to achieve $100 million in annual recurring revenue, having done so 12 months after its founding by MIT graduates in 2023.

"The best LLM applications have an autonomy slider: you control how much independence to give the AI. In Cursor, you can do Tab completion, Cmd+K for targeted edits, or you can let it rip with the full autonomy agentic version." — Andrej Karpathy, CEO, Eureka Labs

Why Software Teams Are Moving to Cursor AI in 2026

The numbers are hard to ignore. In a 2025 survey by Pragmatic Engineer, at least 85% of professional developers currently use some form of AI tool in their workflow. A Stack Overflow survey also indicates that 84% of professional developers currently use or intend to use an AI coding assistant.

What makes Cursor stand out is the depth of integration. Rather than suggesting the next line in isolation, Cursor understands how your entire codebase fits together. That difference produces measurable results:

20–25% time savings on common activities like debugging and refactoring
30–50% reductions in development cycle time on complex, full-stack projects
40% reduction in context switches during coding sessions
30–50% faster developer onboarding firm-wide

NVIDIA's CEO Jensen Huang called Cursor his "favorite enterprise AI service" and reported that 100% of NVIDIA's engineers now use AI coding assistance with a "remarkable boost" in productivity.

"At Innostax, Cursor AI is now part of our everyday engineering workflow. In practice, roughly 90–95% of the code written across projects is AI-assisted, helping teams move faster while maintaining consistent code quality standards."

Main Features of Cursor AI

Tab Completion (Cursor Tab)

Cursor Tab is Cursor's proprietary autocomplete model — not a generic code suggestion engine. It doesn't just complete the current line; it understands your next move based on surrounding code context and recent changes. Developers describe it as autocomplete on steroids.

Inline Edit (Cmd/Ctrl + K)

Inline Edit is Cursor's precision tool for making specific changes to a selected block of code. Highlight a function, describe the change you want in natural language, and Cursor presents a color-coded diff you can accept, reject, or partially apply.

Multi-File, Multi-Step Execution (Agent Mode)

Cursor's flagship capability. Agent Mode accepts a high-level goal in natural language and autonomously writes, edits, tests, and runs code across multiple files. This is Cursor's greatest point of difference from tools like GitHub Copilot.

Codebase Context Chat (Cmd/Ctrl + L)

Cursor uses codebase indexing — reading and mapping your entire project — so AI suggestions account for all files, not just the open one. This powers an AI Chat panel that acts as a project-sensitive collaborator you can query in natural language.

Background Agents and Automations

Released in 2025, Cursor's Automations platform enables teams to configure AI agents that respond to events — not only when triggered manually by a developer.

Cursor AI Pricing: Plans Compared (2026)

In June 2025, Cursor transitioned from a fixed-request model to a credit-based billing system tied to actual model API costs. Current plans:

Plan	Price	Best For
Hobby	Free	Individual exploration
Pro	$20/month	Individual developers
Business	$40/user/month	Engineering teams
Ultra	$200/month	Heavy Agent Mode, large context needs

When to choose Cursor: Teams that want deep, multi-file agent workflows, flexible model selection, and a full IDE experience. Best for complex greenfield or large legacy codebases.

When to choose GitHub Copilot: Teams already embedded in the GitHub ecosystem, or those who primarily need fast autocomplete within existing IDE setups without switching editors.

When to choose Windsurf: Organizations that want on-premise deployment, open-model support, or are cost-sensitive and need a capable free tier.

Integrating Cursor AI Into Your Development Process

Step 1: Installation and Configuration

Download Cursor from cursor.com. Because it is a VS Code fork, all existing VS Code extensions, themes, keybindings, and settings transfer automatically. Integrate your version control (GitHub, GitLab, Bitbucket) and current CI/CD pipelines during initial configuration.

Step 2: Begin with Tab, Advance to Agent

Tab completion is where new users should start — no prompting needed and typing speed improves immediately. Once comfortable, use Inline Edit (Cmd+K) for case-by-case refactors. Reserve Agent Mode for high-level goals where you want Cursor to arrange the implementation autonomously.

Step 3: Pair with Continuous Integration

Integrate Cursor's ability to propose and generate test cases into your CI pipeline. Set up Cursor to run automatic tests at the end of Agent-generated commits. This closes the feedback loop between AI-generated code and verified behavior.

Step 4: Review Every Diff

Never merge AI-generated code without reviewing it. Use Cursor's color-coded diff previews to check every change. For high-stakes refactors, work in a feature branch and commit frequently. Use the built-in bug finder (Cmd+Shift+P → bug finder) to surface regressions before shipping.

Real-World Use Cases

Rapid Prototyping

Cursor Agent Mode can scaffold full features — login systems, REST API endpoints, database models — from a high-level natural language description. Teams report compressing work that previously took days of prototyping into hours.

Legacy Code Modernization

Individual engineers at Coinbase refactored and upgraded entire codebases in days rather than months using Cursor. The codebase-wide context window allows Cursor to recognize outdated patterns and propose modern replacements without sacrificing business logic.

Automated Testing

Cursor analyzes existing function signatures and generates unit test suites, proposing edge cases developers might miss. Combined with CI automation, this significantly reduces the testing bottleneck on high-velocity teams.

Onboarding New Developers

New engineers can explore and understand unfamiliar codebases through natural language queries, without waiting for documentation or senior engineer walkthroughs. Teams report 30–50% faster onboarding using Cursor systematically.

Limitations and Honest Caveats

Cursor AI is powerful, but it cannot substitute engineering judgment. Teams should be aware of the following:

Hallucinated Code — Like any LLM, Cursor can produce plausible-looking but incorrect code, particularly with niche APIs or complex algorithmic logic. Every output requires human review.

Context Window Limits — Large monorepos can exceed context window limits, causing the AI to lose track of distant files. Scoping Agent Mode tasks to specific modules alleviates this.

Privacy Risk of Sensitive Code — While Privacy Mode disables code retention, teams working with trade secrets or regulated data should confirm Cursor's data handling with their legal team before onboarding sensitive codebases.

Internet Dependency — All Cursor AI features depend on cloud API calls. Standard plans do not support offline or air-gapped development.

How This Cursor AI Guide Was Built

This guide was developed by Sahil Khurana, CEO of Innostax, based on direct experience integrating Cursor AI into production engineering workflows across SaaS products, enterprise platforms, and fast-moving startup teams since its mainstream adoption in 2023.

The patterns outlined here — from Tab completion and Agent Mode workflows to CI/CD integration, code review guardrails, and real-world limitations — are drawn from live engineering environments, not demo projects. At Innostax, roughly 90–95% of code written across projects is AI-assisted using Cursor, making this an operational perspective rather than a theoretical one.

Innostax is a managed software development partner that has built AI-native engineering workflows into its core delivery model. We work with CTOs, SaaS founders, and product leaders who want to move faster without sacrificing code quality — typically in situations where development velocity is stalling, AI tooling is being adopted without proper guardrails, or teams need senior engineering oversight on AI-generated output before it ships.

If you're looking to integrate Cursor AI into your engineering workflow in a way that actually improves delivery — without accumulating technical debt — explore our work at innostax.com, or connect directly at innostax.com/contact.

Originally published on the Innostax Engineering Blog.