Hallucination & factuality
Domain-expert verification against ground truth. Confabulation, source misattribution, and confident inaccuracy flagged per output.
FACTUAL · SOURCE · CONFABULATION
Human judgment, [measured].
Domain-expert evaluation with calibrated rubrics, agreement metrics, and regulatory-grade reporting. Text, voice, image, video, and multimodal outputs.
Benchmark scores are not evaluation. They are a starting point most teams mistake for the finish line.
BLEU, ROUGE, and perplexity measure surface properties of text. Crowd-worker ratings drift by the hour. LLM-as-a-Judge grades in the model's own voice. Each method has its place. None of them, alone or combined, will tell you whether your model is ready to ship.
Production readiness needs a different kind of answer.
Why this holds up
Domain specialists, not crowd workers
Radiologists evaluate medical outputs. Lawyers evaluate legal reasoning. Engineers evaluate code. The gap automated metrics leave is exactly where expertise matters.
Rubrics calibrated, signed off before work starts
We co-design the evaluation framework with your team: dimensions, scales, edge cases, pass criteria. Evaluators are calibrated against your gold standard before the first rating is scored.
Agreement reported with every delivery
Inter-rater agreement measured per rubric dimension. Evaluator drift flagged in real time. Disagreements are data, not noise.
Regulatory [precision]
Outputs mapped to EU AI Act risk categories, NIST AI RMF functions, and your internal risk framework. The audit trail ships with the data.
What we evaluate
Safety & red-teaming
Adversarial prompt design and structured safety evaluation by specialists who understand the failure modes in your domain.
SAFETY · ADVERSARIAL · EDGE
A/B preference evaluation
Side-by-side human rating of competing model versions on production-representative prompts. The preference signal that drives model selection.
PREFERENCE · RANKING · PROD
Bias & fairness
Demographic performance disparity analysis across languages, cultures, and user groups. Remediation dataset design on request.
BIAS · DEMOGRAPHIC · FAIR
LLM-as-Judge rubric design
Expert-designed rubrics for automated evaluation pipelines, calibrated against human ratings. Well-designed rubrics are what make LLM-as-Judge trustworthy.
RUBRIC · CALIBRATED · SCORING
Continuous monitoring
Post-deployment human evaluation at regular intervals. Model drift, performance degradation, and emerging failure modes caught before users are affected.
MONITORING · DRIFT · SUSTAINED
How an evaluation program runs
Calibrate
Tell us the model, the output types, the risk categories, and the decisions the evaluation has to support. We co-design the rubric with your team, match evaluators with the domain expertise required, and calibrate against your gold standard before rating begins.
Evaluate
Domain specialists rate outputs with structured rubrics. Every rating carries evaluator ID, timestamp, written rationale, and the specific rubric version used. Agreement is tracked in real time, not after the fact.
Report & iterate
You receive structured evaluation data with per-rubric scoring, agreement metrics, failure-mode breakdowns, and recommendations. For continuous programs, the cadence is weekly or on-demand.
The Human Standard, applied to every rating.
What ships with every evaluation
- Rating
- Per-output score against each rubric dimension
- Rationale
- Evaluator written justification, captured with the rating
- Evaluator
- Verified specialist ID, domain credentials on file
- Rubric
- Version-locked, with calibration data attached
- Agreement
- Inter-rater agreement per batch, per dimension
- Drift
- Per-evaluator consistency tracking over time
- Failure modes
- Categorized with examples and severity
- Regulatory
- Mapped to EU AI Act and NIST AI RMF where scoped
- Report
- Per-delivery summary with recommendations
Every rating is traceable to who gave it, why, and against which calibrated rubric.
Who it's for
Model & research teams
Frontier labs, foundation model builders, and applied ML teams evaluating readiness against production-grade criteria, not just academic benchmarks.
Safety & alignment teams
Red-teaming, adversarial evaluation, and structured safety assessment by specialists who can identify subtle failure modes in your domain.
Regulatory, risk & compliance teams
Pre-deployment audits mapped to EU AI Act, NIST AI RMF, and your internal risk framework. Documentation that satisfies procurement and regulators alike.
Questions
Tell us what to evaluate.
Share the model, the task, and the quality threshold. We come back within one business day with a sample evaluation pass and a scoped plan.