We were tired of watching agents die in the build-and-pray cycle.
Two builders, twelve years working together, seven of them on the same team shipping machine learning at scale. We kept seeing the same thing: a team commits to building an agent, spends weeks on it, and only meets the failure in production. The cost was set long before anyone wrote a test. That is the moment we decided to own.
Agent failures repeat. We built a layer that recognizes them.
A retry loop in a support agent is the same retry loop in a sales agent. The same shapes, over and over, across every domain.
So we stopped treating each agent as a fresh mystery. Our Pattern Intelligence learns a failure class once and recognizes it everywhere after. That is what lets Faultmap run before the build, on your goal, personas, data, and tools, with nothing to instrument.
No eval tool can take that position. They all start after you have committed. We start one step earlier.
A retry loop in a support agent is the same retry loop in a sales agent. We learn the shape once, then recognize it in your domain before you build.
It started with a bill we couldn't explain.
We were building AI agents for ourselves and got a monthly bill we could not split into what we needed and what we didn't. So we went and asked other people — a hundred and twenty CXOs, over about a year. Every one of them had the same problem. Not one of them had a fix.
Nobody overpays for AI out of carelessness. They overpay because working out which model is enough for the task is a research project — so breadth becomes the safe default. That is the gap we built Faultmap to close: know which model is enough, prove it still works, and quote the cost before anyone commits.
Two design partners signed before launch — BuSoft through their CTO, Kezzler through a delivery director who owns his margin. The numbers in our economics are modelled, not measured; proving them is what the first year buys.
The thesis, in one breath
Inside the frontier's accuracy band (93–97% on agentic coding) — at $2.10–6.50 per 1K requests instead of $150–600+. Reliability proven before the build; the savings follow.
The discovery is autonomous — 5M+ patterns, no manual consultants in the loop — which is what keeps exhaustive validation affordable at $10K a year.
Builders who lived the problem.
Founder-market fit is direct here, not adjacent. This is a scar, not a slide.
Rengasami Ramanujam
CEO
Spent a decade building machine learning at the scale of tens of millions of users, then led an AI team across three continents shipping enterprise AI. Writes the core engine and the Pattern Intelligence behind Faultmap.
Balagei G Nagarajan
COO
Twenty years running large technical programs, then ran the 120 discovery interviews that found the problem. Handles product, go to market, and the people side. Bootstrapped a business from zero before this one.
We are bootstrapping, on purpose, while we prove the map on real agents with real design partners. We would rather show you a break point than tell you a story.
Faultmap it before you build it.
Preview 20 validation tests free, or talk to us about a build.