We designed and shipped the simulation layer that teaches enterprise AI how to think — for one of three companies building the most advanced artificial intelligence on earth.
Large language models can write poetry, summarize legal briefs, and pass medical exams. But ask one to process a refund across three connected systems, and it falls apart.
The lab had built one of the most powerful AI models in the world. Now they needed it to handle real business operations. But you can’t train an AI agent inside a live business. You need a safe, realistic copy of the real thing.
Each layer solved a distinct engineering problem. Together they created the infrastructure that teaches AI to operate inside real business software.
We built 12 fully functional replicas of real business applications — each running as a self-contained, containerized environment that the AI could interact with exactly as a human employee would.
Every environment had to behave identically to the real product. A payment that fails in production had to fail in the simulation for the same reason.
12 production-grade environments · Full API fidelity · Containerized and reproducible
We designed over 800 scenarios — each a specific business operation with a single, provably correct outcome. Every task was graded on a difficulty curve.
The hardest part isn’t writing tasks. It’s writing tasks where you can prove the answer is correct.
800+ verified scenarios · Difficulty-graded progression · Automated correctness proofs
We built a verification layer that evaluates every agent attempt in real time — checking not just the final outcome, but the sequence of actions taken to get there.
Verifiable rewards are the bottleneck and the breakthrough. Without them, training stalls. With them, the AI improves on every single attempt.
Real-time correctness checking · Process-aware verification · Fully autonomous training loops
We deliberately engineered the messiness of real operations into every environment. Incomplete data. Race conditions. API timeouts. Conflicting business rules.
The difficulty wasn’t technical. It was epistemological: understanding what makes real business operations hard.
Real-world edge cases · Multi-system coordination · Failure recovery scenarios
The containerized architecture meant any environment could be spun up in seconds, run hundreds of agent attempts, and tear down cleanly.
AI products built on models trained inside our environments are now used by billions of people worldwide.
Continuous training pipeline · Thousands of parallel runs · Core infrastructure for model improvement
“We didn’t build AI. We built the world it practices in — and the scorekeeper that tells it whether it got the answer right.”
Production-grade replicas of real business software
Each with a provably correct answer
AI products trained inside our environments
Fully automated training and verification