Evidence-based model selection
Compare candidate models on the behaviors and scenarios that matter to your product.
INDEPENDENT AI PERFORMANCE ASSURANCE
Know how your model performs before users do.
Infinity designs and operates rigorous model evaluations across quality, safety, robustness and domain accuracy—turning model behavior into evidence your product, engineering and governance teams can act on.
Relevant benchmarksTests aligned with real use
Blinded reviewUnbiased model comparison
Failure insightRoot causes, not only scores
Release evidenceDefensible go/no-go decisions
A leaderboard score cannot predict your product experience.
General benchmarks rarely reflect your users, data, risk profile or definition of quality. Infinity builds evaluation systems around the intended use of the model, combining automated measures with calibrated human and expert judgment to reveal how it behaves where it truly matters.
THE DECISION ADVANTAGE
Turn uncertain model behavior into release confidence.
We connect every metric to a user scenario, risk or acceptance criterion so teams can select, improve and release models with shared evidence.
Safer releases
Identify critical risks, failure patterns and unacceptable behaviors before production exposure.
Faster debugging
Error taxonomies and traceable examples show engineering teams where improvement is needed.
Reliable regression control
Repeatable test suites reveal whether a new model, prompt or pipeline change caused quality loss.
Domain confidence
Specialist review validates factuality and reasoning where automated metrics are insufficient.
Clear release decisions
Scorecards combine performance, safety, cost and latency evidence into an accountable readiness view.
One evaluation partner across the complete model lifecycle.
Evaluation strategy
Translate product objectives, model risks and user expectations into a defensible evaluation plan.
Benchmark design
Build representative test sets, golden answers and challenge cases aligned with real-world use.
Rubric-based assessment
Score accuracy, relevance, completeness, reasoning, style, safety and instruction following.
Comparative evaluation
Run blinded side-by-side comparisons across models, prompts, versions and retrieval strategies.
Safety & policy testing
Measure harmful output, refusal behavior, bias, privacy risk and policy compliance.
Robustness testing
Stress-test ambiguity, noisy inputs, multilingual prompts, adversarial behavior and edge cases.
Expert validation
Use qualified domain reviewers for clinical, scientific, financial, legal and technical claims.
Production monitoring
Evaluate sampled live interactions, detect regression and track model quality after release.
From evaluation design to continuous regression control.
Use-case & risk discovery
Evaluation framework design
Benchmark construction
Rubric calibration
Automated metric runs
Human evaluation
Expert & safety review
Error analysis
Release scorecard
Continuous regression testing
MEASUREMENT FRAMEWORK
Balanced metrics for quality, risk and business reality.
Each program uses a fit-for-purpose metric portfolio. Automated results provide scale; calibrated reviewers explain nuance; experts validate high-risk claims and adjudicate uncertainty.
Task successFactual accuracyInstruction followingPreference win rateSafety severityCalibrationRobustnessFairnessLatencyCost per outcome
RELEASE READINESS & GOVERNANCE
Evaluation evidence that survives scrutiny.
We preserve evaluation versions, reviewer decisions, benchmark provenance, known limitations and acceptance thresholds so results remain reproducible and auditable.
- Versioned benchmarks and immutable test sets
- Documented rubrics and acceptance thresholds
- Model, prompt and configuration traceability
- Blind review and conflict-of-interest controls
- Failure severity and escalation records
- Release scorecards and known limitations
Baseline. Diagnose. Decide. Monitor.
Frame
Define use cases, risks, target behaviors, candidate models and decision criteria.
Build
Create benchmarks, rubrics, gold answers, challenge sets and reporting logic.
Evaluate
Run automated, human, expert, safety and robustness assessments.
Control
Deliver release evidence and maintain regression tests as systems evolve.
Build an evaluation system your teams can trust.
Share your model, application, users, risks, candidate versions and release timeline. We will structure the complete evaluation program.
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