01AI PRODUCT
Instrument real work
Equip experts with the best available AI tools to complete complex engineering, knowledge, and research work—capturing permissioned trajectories, artifacts, corrections, and outcomes.
LOG MINING · EVALUATION · TARGETED DATA · INFERENCE
The improvement
infrastructure for long-horizon AI work.
RecipeLab mines execution logs to find where AI systems fail, builds the evaluations and data that improve them, and verifies progress on real work.
RLIMPROVEMENT
ENGINE
ENGINE
01REAL WORK
02LOGS
03VERIFIER
04DATA
Software · knowledge work
research
THE SHIFT / 001
Real work unfolds
over hours or days.
Complex software engineering, knowledge work, and research all require persistent context, sequential decisions, extensive tool use, recovery from mistakes, and artifacts that can be tested or reviewed.
“The missing asset is not another benchmark.
It is a learning loop built from real work.”
THE PLATFORM / 002
Three layers.
One improvement loop.
Experts accomplish valuable work.
RecipeLab turns the process into improvement.
02EVALUATION
Turn logs into learning signal
Mine traces for recurring failure patterns. Fast Verifier scores decisions, evidence, intermediate artifacts, and outcomes; Asker creates probes and recommends what data to build next.
03INFERENCE
Run the loop economically
Make agent rollouts, log replay, verifier passes, data generation, long-context execution, and repeated model experiments economically practical.
HOW IT WORKS / 003
Mine the process—
not only the answer.
Fast Verifier converts raw traces into structured scores and failure labels. Asker turns recurring weaknesses into probes, evaluations, and recommended training recipes.
Instrument real work
Capture plans, tool calls, artifacts, errors, retries, corrections, and outcomes.
Mine execution logs
Identify recurrent failure patterns and successful recovery strategies.
Build evaluations
Convert real tasks into private, held-out tests of complete workflows.
Create targeted data
Generate expert, synthetic, or replayed trajectories for diagnosed gaps.
Post-train
Apply the intervention to improve the model or system.
Verify improvement
Measure whether the full workflow is genuinely better, then repeat.
THE FLYWHEEL / 004
Every deployment creates
the evidence for the next.
Real execution
logs×Verifier + Asker +
targeted data=Recursive system
improvement
logs×Verifier + Asker +
targeted data=Recursive system
improvement
DESIGN PARTNERS / 005
Bring us the work
your AI must master.
We instrument the workflow, mine the logs, build private evaluations, create targeted data, and demonstrate measurable improvement.
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