No Ads, No Launch, No Hype: How Jito Chadha Grew Nventr to Process Trillions in Transactions

Jito Chadha spent years quietly building Nventr inside real operating environments, letting the platform prove itself under actual conditions before anyone outside those organizations knew it existed. Today, Nventr processes nearly a trillion dollars in transactions annually.

Nventr is a workflow automation and AI platform designed to formalize the way complex organizations actually function. Rather than layering software on top of broken processes, Chadha built structured, deeply connected workflows that can scale automatically and now support AI agents with enterprise-grade controls around data permissions and security.

What makes Chadha’s story worth paying attention to is his deliberate resistance to the startup playbook. No ads, no campaigns, no premature launch. Just years of building under real conditions and letting adoption happen organically. In the interview below, he talks about what it took to build a platform that could run at enterprise scale, why most organizations are deploying AI dangerously, and what patience actually looks like when you’re building infrastructure meant to last.

Tell us about yourself and your business.

I’m a technologist focused on building structured workflows, automation, and AI systems that operate at real enterprise scale. My work centers on understanding how complex organizations actually function and designing systems that make them more efficient, scalable, and secure in practice. I spend most of my time thinking about workflows, data movement, and how technology can reduce latency and friction inside large, distributed businesses.

My company is Nventr. We build a platform that combines deeply structured workflows with AI agents to automate end-to-end business processes. The platform has been live for years and runs nearly a trillion dollars in transactions annually, supporting thousands of full-time employees and hundreds of thousands of contractors. Today, we’re expanding that foundation with AI agents that give organizations a controlled way to modernize, from large enterprises to individual entrepreneurs.

How does your business make money?

We make money by selling access to the Nventr platform as a software product. It’s delivered as a suite of modules, and pricing scales based on usage and number of users. In larger organizations, that typically means allocating access across teams or entire departments.

We’re also opening the platform to individual users and smaller teams through self-service sign-up. Even a single employee using the platform can become an entry point for broader adoption. Over time, that per-user usage compounds as workflows and agents take on more real work.

What was your inspiration for starting the business?

The inspiration came from operating inside large, complex organizations and seeing how much work was happening through disconnected steps and fragile processes. Most businesses already function like multi-step workflows; they’re just not built intentionally or efficiently. That gap between how work actually happens and how systems are designed was the starting point.

We built Nventr to formalize that reality. The goal was to create structured workflows that could connect everything together, scale automatically, and eventually support AI without breaking security or data permissions. As AI became more powerful, we wanted to give organizations a way to do it properly, with guardrails.

How and when did you launch the business?

We didn’t launch in the traditional sense. The platform was built and put into production years ago to support real operating companies. From the beginning, it was used to run live workflows and move large volumes of data and transactions. That allowed us to test and refine the system under real conditions long before thinking about marketing or growth.

For most of its life, adoption happened internally through advocacy inside those organizations. Only recently have we started preparing for broader access as the platform matured and expanded into agents.

How is the business funded?

The platform was built and supported inside real operating environments rather than through a traditional fundraising process. It was developed to service portfolio companies that were already running at scale, and sustained through real usage and operational needs.

The priority was to build infrastructure that worked in production and let adoption grow organically. Only now are we opening the platform more broadly.

How did you find your first few clients or customers?

Our first customers came directly from the environments we were already operating in. We built the platform to service portfolio companies with real operational complexity, and those teams used it day to day to run workflows and support real business processes.

Adoption grew organically as people saw value and advocated for it internally, across teams, and eventually with external collaborators. The product spread because it worked, not because it was sold.

What was your first year in business like?

The first year wasn’t really about starting a business in the traditional sense. The focus was entirely on designing and deploying workflows that could support real operations inside large, complex environments. Everything we worked on was production-oriented, solving immediate problems around scale, data movement, and reliability rather than thinking about branding, marketing, or growth.

Because the platform was being used inside active operating companies, value was being created from the beginning. The work was iterative and practical. We were constantly refining workflows, handling edge cases, and making sure the system could scale and recover reliably. The emphasis was on getting the core right, not on speed to market or external visibility.

There was no clear separation between building and using the product. The early phase was defined by deep involvement in the details of how businesses actually run and translating that into structured, repeatable systems. That foundation is what allowed everything else to come later, including broader adoption and expansion beyond those initial environments.

What strategies did you use to grow the business?

Our primary strategy was to grow by building something that worked under real load. We focused on servicing portfolio companies with real operational complexity and let the product prove itself inside those environments. That meant running live workflows, moving large volumes of data, and supporting real people doing real work every day. Growth came from usage, not from positioning.

Inside those companies, adoption happened organically. Independent management teams and boards used the platform, saw value in it, and started advocating for it internally. From there, it spread across teams, departments, and eventually to customers and third-party collaborators. That internal pull was far more important than any outbound effort. It allowed the product to evolve based on real needs rather than assumptions.

We were very intentional about not chasing growth too early. We did not run ads, we did not do campaigns, and we did not even offer self-service sign-up. We spent years refining workflows, reliability, automation, and security before worrying about broader exposure.

Another key part of growth was focusing on structured workflows as the foundation. That is the bread and butter of the platform. By solving concrete, painful problems around how work actually moves through an organization, the platform naturally became sticky. Once a workflow is embedded deeply into how a business operates, it is very hard to replace.

More recently, the strategy has shifted toward outside organic growth. As the platform matured and expanded into agents, we started preparing for self-service access and a broader audience. The idea is still the same, let real users discover value, start small, and expand usage organically over time. Even a single employee using the platform can become the entry point for much larger adoption.

What was the biggest challenge you had to overcome?

The biggest challenge was modernizing organizations with AI while doing it the right way. Employees were already using AI tools without oversight, exposing sensitive data in ways companies didn’t fully understand. The challenge was enabling AI while maintaining control over data permissions, user permissions, and security at enterprise scale.

Another challenge was knowing when to open the platform more broadly. We focused on building and running real systems for a long time before worrying about growth. Deciding when the foundation was strong enough to support wider adoption without compromising those principles was not trivial.

What have been the most significant keys to your business’ success?

The most significant key to our success has been staying focused on real systems and real usage. We built the platform inside live operating environments and let it be shaped by actual business needs. Running real workflows at scale forced us to solve hard problems around reliability, security, and complexity early. That foundation made the technology durable and practical, not theoretical.

We resisted the urge to launch early, market aggressively, or chase growth before the product was ready. By prioritizing structured workflows, guardrails, and scalability first, adoption happened organically. When people see technology that actually makes their work easier and more efficient, they advocate for it themselves. That internal pull has been far more powerful than any traditional growth strategy.

Tell us about your team.

The team is large and highly distributed. We support around 10,000 full-time employees who operate inside workflows connected to the platform, along with roughly 200,000 freelancers who scale up and down to handle manual review and data-related work.

On the product side, engineers use the platform daily to build pipelines and deploy into auto-scaling, auto-healing cloud environments. The system is designed for global, remote, asynchronous work rather than centralized teams.

What is the most important lesson you’ve learned growing the business?

The most important lesson I’ve learned is that you have to build under real conditions. It’s one thing to design something that looks good on paper or works in a demo. It’s another thing entirely to run real workflows, move real data, and support real people at scale. When you operate inside live systems, the problems become very clear very quickly. That pressure forces better decisions around structure, reliability, and security.

The other part of that lesson is patience. We resisted the urge to launch early, market aggressively, or chase growth before the foundation was right. By focusing on structured workflows and getting the core right first, everything else followed naturally. When technology actually works and makes people’s jobs easier, adoption happens on its own. That internal pull is far more powerful than trying to manufacture growth before the product is ready.

What separates your business from your competitors?

What separates us is that we build for real operations, not demos or point solutions. The foundation of our platform is structured workflows that can run many layers deep, with loops, branches, and independently scaling components. Most organizations already operate this way whether they realize it or not. We just give them a cloud-native way to do it intentionally, at scale, and in production.

Another major difference is how we approach AI. We’re not just adding AI on top of existing tools. We focus on hybridizing the entire organization by pairing people with agents while maintaining strict control over data permissions and user permissions. If you want to run a competitive organization in the modern age, you need AI, but you also need guardrails. We built the platform so companies can modernize safely, without exposing enterprise data or losing control of how information moves.

Finally, we’ve proven this under real load. The platform has been running live workflows for years, supporting large, distributed teams and moving massive volumes of data and transactions. A lot of tools look impressive in isolation. Very few are built, tested, and refined inside real operating environments before being offered more broadly. That history is a big part of what sets us apart.

What are your future plans for the business?

The focus now is on opening the platform up more broadly after years of building and running it inside real operating environments. We wanted individuals, small teams, and companies to start using the platform without heavy procurement or long sales cycles. The idea is to let people start small, discover value quickly, and expand usage naturally over time.

A big part of that future is the agent product. Nventr Agent and Agent IO are about hybridizing the organization by giving people AI agents that can actually learn their jobs and work inside structured workflows. That means pairing automation with guardrails, permissions, and centralized control so companies can modernize safely rather than in an ad hoc way.

Longer term, the goal stays the same. Build infrastructure that reflects how work actually happens, scale it responsibly, and let adoption grow through real usage. We want to support everything from individual contributors to large enterprises, all on the same foundation, without compromising security, structure, or reliability.