Why Good AI Ideas Die Before Production | Balaji V

What we talked about.

Sunny Ray talks with Balaji V of TechWitty about why promising AI products stall between demo and production. Balaji explains that after working with over 200 founders across six years, he sees most products fail in the deployment environment, especially in regulated settings like healthcare where permissions, data security, and compliance matter. He shares how his earlier real estate ventures, including Cozy Homes and Munk Spaces.AI, shaped the approach, and admits that an early AI launch in Singapore left a client team awake for about 72 hours. The conversation covers the difference between an investor demo and a product that survives real users, why runaway agents and weak context layers break first, and how startups engage a product partner at different stages. Balaji also describes expansion plans toward the West Coast and Texas, a health tech vertical, and the moment after a founder signs two enterprise customers when long term partnerships form. He closes with his view of a lean five person product team in five years.

Most AI products don't fail because of the model. Balaji V explains why deployment, context, and compliance kill good ideas before launch.

The questions, and the answers.

Where exactly do good AI ideas die between research and production?

We have assessed and worked with over 200 founders over the last six years, and what we found is that getting a product running is easy today. Products die in the deployment environment, whether that is a mass market B2C rollout or a heavily regulated B2B customer such as healthcare. You can't plug in something vibe coded and expect to skip permissions, caching, environment-based data security, and compliance.

What did going AI first force you to stop doing?

The first thing we stopped was adding twenty new features that customers around the world kept asking for. Between 2019 and 2022 we were a custom solution provider, and that fell apart in the age of AI. Now we gather the data, put memory in place, and add custom context layers that scale on their own. We go outcome first, then decide between an agent, a human in the loop, or a deterministic outcome.

When did you realize the hard part of AI products is production and not the model?

I'll be candid. The first versions of our AI product were put together by me and a non-technical co-founder, and we went live in Singapore. We made mistakes to the point where the client's whole team was awake for about 72 hours. Data wasn't stored correctly and agents misbehaved. The models weren't the issue; the guardrails and harness weren't where they needed to be with our first two customers.

What separates a demo that impresses investors from a product that survives real users?

A demo runs on a set of data and assumes a client will follow one workflow. In reality, hospitals and healthcare systems each function very differently. So we tell founders to understand the workflow and where the context resides, instead of plugging in a product they already built. We go data first and then production, rather than production first and then augmenting with data.

When an AI product fails after a great demo, what usually breaks first in production?

Runaway agents. Either the agent starts doing tasks that were not required, which is becoming evident even with the largest companies and frontier models, or it lacks the right brain, which is the context and data layer. Those are the two biggest problems I see: the agent either can't bring a task to completion, or it does something completely different from its purview.

What has to happen after launch for a startup to see you as a long term partner, not just the team that built the first version?

It's usually when they sign their first two enterprise customers. Four hours of a twelve hour workday go into dealing with each customer, so two customers eat eight hours on paperwork, agreements, testing, demos, and buy-in from eight people in the organization. At that point they set aside tasks for us to handle while they focus on keeping a long term partnership with those clients.

In five years, what does a startup's product team look like when AI writes much of the code?

I think a five member team should handle what a fifteen member team handled two or three years ago. Engineering is only one part. QA becomes AI safety, and DevOps becomes AI ops. You would have a designer, an FDE who is essentially a product manager, someone in AI safety and ops, someone in compliance, and the core in-house engineer.