A practical framework for defining representative cases, scoring outputs, testing failure modes, and monitoring an AI workflow over time.
Clean build · Fast delivery · Scalable foundation · Less burn
Built for
Teams moving an LLM or AI feature from prototype to reliable product behavior.
Intended outcome
A repeatable quality process connecting model behavior to user impact, risk, latency, and cost.
Representative evaluation set
Task-specific scoring
Regression and production monitoring
Collect real representative examples
Define acceptable and unacceptable output
Combine automated and human scoring
Run evaluations whenever the system changes
Most founders bring a monthly budget and hire us to deliver. A few bring a vision strong enough for us to join the bet. Both models stay lean, direct, and accountable.
Build + maintain
You set the cash ceiling. We cut the scope to fit, ship working software every week, launch it, and keep it healthy. No hourly mystery. No hostage code.
Virtual CTO partnership
For a small number of serious, long-horizon products, we join as the technical partner: roadmap, architecture, hiring, delivery, and scale. Lower cash. Real equity. Shared upside.
User feedback arrives after exposure, is often sparse, and may not cover dangerous or rare failure modes. Pre-release evaluation makes quality an engineering input.
Sometimes, with a validated rubric and calibration against human judgment. It should not be assumed reliable for every task or risk level.
Common cases, difficult edge cases, known failures, adversarial inputs where relevant, and examples representing different users, formats, and contexts.
A practical PRD structure for defining users, outcomes, workflows, constraints, evidence, scope, and open decisions.
A scope framework for separating the essential learning workflow from attractive but non-critical features.
A structured review of architecture, data, integrations, security, operations, delivery, and unresolved technical risk.
Bring the workflow, idea, or delivery mess. We’ll cut it to the leanest credible build and tell you which partnership model fits.