Separating practice from the final exam
Training data and exam questions must never touch. We freeze a held-out test suite before training starts so the model cannot game the test.
Evaluation and promotion
An honest exam before anything touches production.
A training curve only tells you that a model changed. An independent exam tells you if it actually got smarter or just learned to cheat. We test checkpoints against frozen problems they have never seen before and give you a clear before-and-after scorecard.
Held-out secret test suite
Before & After scorecard
Hard sign-off gates
Immutable audit trail
Why g factor
Why own the weights instead of renting an API?
Score real artifacts, not 'AI vibes'
We don't ask an LLM judge if the answer sounds polite. We run the unit tests, verify the CAD geometry, and inspect the simulator telemetry.
100% reproducible decisions
Every scorecard is permanently tied to the exact weights, tokenizer, test set, and git commit. Anyone on your team can verify the math.
Capabilities
Evidence for every promotion decision.
Benchmark plans
Define the evaluation contract before inspecting results and preserve it with the run.
- Frozen task and environment revisions
- Declared metrics and hard gates
- Repeatable seeds and execution settings
Matched comparisons
Evaluate the base and adapted policies on the same contract so improvements and regressions are attributable.
- Shared held-out task set
- Correctness, robustness, cost, and latency views
- Explicit failure and uncertainty reporting
Promotion evidence
Keep the decision tied to exact artifacts instead of treating a successful training job as deployment approval.
- Immutable adapter and checkpoint identity
- Environment and verifier receipts
- Reviewable approve-or-reject outcome
Before you start
Common questions
How is LLM evaluation different from training reward?
Training reward is like doing homework practice: it guides the model as it learns. Evaluation is the final exam with no answers in the back of the book. High training scores mean nothing if the model fails the secret test exam.
How do you compare a base model with a fine-tuned adapter?
We run both on the exact same secret test questions with identical compute limits. You get a side-by-side scorecard: task completion rate, speed, token efficiency, and regression tracking.
What makes a model qualification reproducible?
Every decision locks together the model weights, tokenizer, simulator version, prompt hash, and random seeds. Any engineer on your team can rerun the test and get the exact same numbers.
Private beta
Turn your workflow into a skill your model can own.
Bring a model and a task that matters to your company. We'll tell you honestly if post-training fits.
Request Demo