As the landscape of enterprise technology shifts toward hyper-specialization, the traditional boundaries between general business tools and industry-specific solutions are dissolving. Vijay Raina, a seasoned expert in enterprise SaaS and software architecture, has been at the forefront of this transformation, observing how platforms evolve to meet the grueling demands of modern commerce. In a world where off-the-shelf software often falls short of meeting complex regulatory and operational needs, the move toward “Vertical SaaS” represents a significant pivot in strategy. Today, we sit down with Vijay to discuss the recent, massive expansion into seven distinct industrial verticals—ranging from the high-stakes world of banking and healthcare to the fast-paced retail and restaurant sectors—and how integrated AI is finally providing the contextual intelligence businesses have craved for decades.
You are making a massive, simultaneous push into seven different markets, from the automotive sector to healthcare. How do you decide where to direct the most significant investment, and what metrics are you using to determine which of these verticals deserves even further expansion?
The decision isn’t based on a whim or a simple market forecast; it’s rooted in the natural traction we have observed within our ecosystem over the last few years. We noticed that certain sectors, particularly financial services, automotive, and healthcare, were already showing a deep hunger for more specialized tools. To manage this, we adopted a co-creation model, where our engineers and architects literally sit down with customers to dissect their specific daily challenges. This hands-on approach gave us the “domain authority” necessary to move forward, because you cannot simply guess your way into the banking or insurance sectors. Success for us isn’t about the vanity metric of moving from seven verticals to seventy; it is about the depth of the integration. We are looking at how successfully we can become an end-to-end technology provider for a company, ensuring that our vertical solutions aren’t just add-ons, but the central nervous system of their operations.
When you talk about the importance of depth, it raises an obvious question: why launch all seven of these initiatives at once? Many would argue it is more effective to perfect two or three industries before scaling further.
What might look like a sudden “big bang” launch to the public is actually the culmination of an immense amount of “behind the scenes” work that has been quietly humming for four years. Since the early 2020s, we have been embedded with major players like Mercedes-Benz and Chola to understand the nuances of their workflows. We didn’t want to release half-baked products; we waited until we had successfully productized these solutions based on real-world testing and feedback. By launching them together now in 2026, we are signaling that our broader foray into verticals is a unified strategic addition to our horizontal suite, rather than a series of disconnected experiments. This timeline allowed us to build the necessary expertise so that when we finally flipped the switch, the product was already mature enough to handle the rigors of global enterprise standards.
Regarding the current customer base within these new verticals, are you primarily seeing adoption from long-time users of your horizontal suite, or are these industry-specific tools attracting entirely new organizations?
The trend varies significantly depending on the industry, but we are seeing a fascinating “gateway” effect. In the automotive sector, we focus on a small number of large OEMs, whereas in retail, our footprint already extends to thousands of businesses. For many healthcare organizations or restaurants, these vertical tools are their first introduction to our brand. They come for the specialized healthcare management system or the restaurant POS, and then they have this “lightbulb moment” where they realize they can integrate their mail, surveys, and accounting through the same ecosystem. This creates a two-way momentum: sometimes the CRM leads them into the vertical, and other times the vertical solution is the “land” that allows us to “expand” into their entire back-office infrastructure.
What do you believe is the primary advantage of a broad platform company over the specialized, niche SaaS providers that have traditionally dominated these individual industries?
The Achilles’ heel of many specialized vertical SaaS providers is that they have become siloed, legacy islands. Many of these niche tools were built a decade ago and lack modern APIs or the Model Context Protocol (MCP) integrations required to communicate with contemporary AI. When a healthcare provider or a bank wants to adopt generative AI today, they often find themselves “stuck” because their core industry tool doesn’t have the security layers or the connectivity to support it. We are challenging that status quo by offering the best of both worlds: the horizontal breadth of a world-class infrastructure combined with deep, industry-specific functionality. This prevents the data fragmentation that occurs when a business is forced to stitch together a dozen different “best-of-breed” tools that don’t actually talk to each other.
As AI giants move toward industry-specific solutions and software development becomes increasingly automated, how do you maintain a competitive moat?
There is a common misconception that because AI can write code faster, anyone can build a vertical SaaS solution overnight. However, building a “system of record”—the core database for a loan processor or an HR department—is a very deep, complex play that involves more than just lines of code. For example, when we worked with a Non-Banking Financial Company (NBFC) to build their loan processing system, we had to account for over 200 different variables, including complex compliance checks, pricing logic, and regulatory reporting. That isn’t something a generic AI can hallucinate; it requires “been there, done that” institutional knowledge and years of sitting in the room with compliance officers. Our moat isn’t just the software; it’s the intelligence and experience we’ve baked into the business rules that govern how that software operates in a high-stakes environment.
Do you see these vertical products maturing into standalone revenue engines, or will they always function primarily as a way to drive adoption of your broader suite of applications?
They are already proving to be significant revenue streams in their own right, and I expect that trend to accelerate. When you win a deal with a major OEM or a national retail chain, these are often large-ticket contracts worth millions of dollars, which puts them on a similar financial trajectory as our core CRM products. That said, the “ecosystem effect” is a massive multiplier for that revenue. In the retail space, for instance, we can offer a unified package of ERP, POS, and payments where every single piece of data flows seamlessly between applications. We even offer our POS for free to smaller restaurants making up to ₹12 lakh annually, knowing that as they grow, they will naturally look to our integrated inventory and marketing tools to scale their business.
By diving so deep into seven different industries, is there a risk that the overall platform will lose the simplicity and seamless integration that made it popular in the first place?
It is a valid concern, but I view it as a convergence rather than a deviation. At the end of the day, most global commerce boils down to about 15 to 20 major business types, all of which share certain core DNA even if their “flavors” are different. By going deeper into retail or education, we aren’t moving away from our core; we are actually strengthening the underlying platform to be more flexible and robust. The momentum is two-way: the lessons we learn from the complexities of automotive manufacturing eventually trickle back down to improve our horizontal project management and supply chain tools. We are essentially building a more powerful, more adaptable engine that can handle specialized tasks without sacrificing the unified user experience.
Regarding the Zia AI agents, what kind of tangible value do you see when they begin executing industry-specific workflows rather than just acting as a general assistant?
The productivity gains are where this gets really exciting and, frankly, quite emotional for the people doing the work. We are seeing processes that used to take a human 45 to 50 minutes of tedious manual entry and cross-referencing being completed by Zia in just four or five minutes. When you remove that level of friction, you aren’t just saving time; you are improving accuracy and allowing professionals to focus on the “quality work” they were actually hired to do. The goal of Zia Chat and our MCP integrations is to provide the intelligence needed to make decisions instantly, rather than forcing a user to hunt through three different systems to find a single piece of contextual data.
As these vertical products provide more specialized context to your AI, do you envision a future where these agents become highly specialized experts for each specific industry?
Absolutely, and that is where the real “contextual intelligence” resides. A generic AI doesn’t know how to handle an NPA (Non-Performing Asset) report in the banking sector or how to classify a specific medical emergency in a healthcare setting. Our specialized agents for the BFSI sector, for example, are being trained to understand the prevailing norms, regulatory constraints, and specific reporting requirements of that world. As a product vendor, we are constantly refining these agents so they know exactly what data can be shared and what must be protected. This combination of horizontal AI capability and vertical industry knowledge is what will ultimately define the next generation of enterprise software.
What is your forecast for the future of vertical SaaS integration?
I believe we are entering an era of “Invisible Infrastructure,” where the distinction between a horizontal tool and a vertical application completely vanishes for the end user. In the next few years, businesses will no longer accept the “integration tax”—the time and money spent trying to make disparate systems work together. Instead, we will see a massive consolidation toward platforms that can provide deep, industry-specific “contextual intelligence” right out of the box. The winners in this space won’t just be the ones with the best AI, but the ones who own the most reliable systems of record and can prove they understand the 200-plus variables that make a specific industry unique. Specialized software will move from being a “tool you use” to an “environment you inhabit,” where AI agents handle the mundane complexity, leaving humans to manage the strategy and the relationships.
