Know Exactly Where
Your Model Stands
Independent, human-powered evaluation that goes beyond automated metrics — exposing real failure modes before your model hits production.
Comprehensive Model Evaluation Services
Automated benchmarks tell you what a model scores. We tell you why it fails — and what to do about it.
Human Preference Evaluation
Side-by-side and Best-of-N comparisons rated by trained human evaluators — the gold standard for LLM alignment, summarisation, and dialogue quality.
Failure Mode Analysis
Systematic red-teaming and adversarial probing to surface hallucinations, refusals, prompt injections, and edge-case regressions before they reach users.
Fairness & Bias Auditing
Evaluation across demographic slices, linguistic varieties, and protected attributes — with actionable reports aligned to EU AI Act and NIST RMF standards.
Benchmark Construction
Custom holdout sets and leakage-free evaluation suites designed around your specific domain, task taxonomy, and quality bar.
Safety & Guardrail Testing
Structured testing of content filters, PII detectors, and instruction-following constraints against curated adversarial prompt libraries.
Regression Tracking
Versioned evaluation runs with delta reporting so every model checkpoint is objectively compared against your production baseline.
What We Measure
Our Evaluation Workflow
Structured, reproducible evaluations with clear deliverables at every stage.
Define Scope
We work with you to define the evaluation task, quality rubrics, annotator profile, and pass/fail criteria before any evaluation begins.
Benchmark Design
Construction of leakage-free test sets spanning typical, difficult, and adversarial inputs — representative of your production distribution.
Expert Evaluation
Domain-qualified annotators assess outputs using structured rubrics with calibration sessions and inter-annotator agreement monitoring throughout.
Analysis & Reporting
Slice-level breakdowns, confusion analysis, and failure taxonomy delivered in an executive summary plus raw data export.
Iterate & Track
Re-evaluate on the same benchmark after each training cycle to objectively track progress and catch regressions.
Who This Is For
LLM & Foundation Model Teams
RLHF preference data, instruction-following audits, and safety red-teaming for pre-training and fine-tuning runs.
Computer Vision Products
Detection, segmentation, and classification accuracy audits across lighting conditions, occlusion, and geographic diversity.
Healthcare AI
Clinical NLP and medical imaging evaluation with HIPAA-aware workflows and regulatory documentation support.
Autonomous Systems
Sensor fusion model evaluation across edge cases — night driving, adverse weather, rare pedestrian scenarios.
Need an independent evaluation of your model?
Share your model, task, and quality bar — we'll design a human evaluation plan and deliver results within your timeline.