Accuracy you can defend in a review.
QA, validation, benchmarking, and transcription — with documented rubrics, audit trails, and statistical significance. Your model's performance numbers should survive any peer review.
Benchmarks that tell the truth.
Public benchmarks get gamed and saturated. Real-world evaluation requires custom suites built by domain experts, statistical design, and honest calibration against human judgment.
We design evaluation programs that measure what your model is actually doing — factuality, reasoning depth, safety boundaries, code correctness, agent task completion — and deliver the data you need to ship with confidence.
What we evaluate.
Multi-media transcription
Verbatim, clean, timestamped, and diarized transcription in 90+ languages.
Native-language transcribers, not outsourced templates. Punctuation, disfluencies, code-switching, and speaker changes captured the way a human actually heard them.
Verbatim transcription
Every hesitation, repair, and filler word captured for conversational modeling.
Clean transcription
Polished readable text for captions, training corpora, and content workflows.
Speaker diarization
Multi-speaker identification with timestamped speaker-change boundaries.
Force alignment
Word-level timestamps for TTS, dubbing, or accessibility workflows.
Model benchmarking
Side-by-side evaluations with statistical rigor and expert adjudication.
Blind A/B/n evaluations by domain experts, with sample sizes calculated for statistical power, rubrics designed for the task, and full disagreement analysis in your final report.
Head-to-head model comparisons
Blind ranking of your model against competitors or internal variants.
Version regression
Automated eval runs to catch quality regressions between releases.
Capability mapping
Granular scoring across reasoning, factuality, creativity, safety, code.
Holistic scorecards
Single-number dashboards with breakdowns for leadership reporting.
Search relevance
Query-result grading for rankers, retrieval systems, and RAG pipelines.
Relevance is judgment, not just click-through. We provide human-labeled relevance data for training rankers, tuning retrieval, and auditing your RAG system's actual quality.
Query-doc grading
5-point scale relevance labels with rater calibration across domains.
Intent classification
User intent categorization for query understanding and routing.
RAG answer quality
Faithfulness, completeness, and groundedness scoring for RAG outputs.
Side-by-side ranker eval
Pairwise preference labels to train and evaluate learning-to-rank models.
Audio & speech evaluation
WER, fluency, pronunciation, and subjective quality scoring at scale.
ASR and TTS evaluation by native speakers with phonetic training. Mean opinion scores that reflect real listener experience, not proxy metrics.
WER scoring
Standard and weighted word error rate across accents and acoustic profiles.
MOS ratings
Mean opinion scores for TTS naturalness and intelligibility.
Pronunciation scoring
Phoneme-level assessment for pronunciation training and language learning.
Fluency & prosody
Expert phonetician evaluation for synthesized speech quality.
Evaluation is engineering.
Every eval project follows a statistical protocol designed with your team — not a canned benchmark template.
Rubric design
Define scoring criteria, scales, and edge-case handling with your team.
Sample sizing
Power analysis to determine dataset size for statistical significance.
Rater calibration
Gold-set training until all raters converge on agreement thresholds.
Blind evaluation
Model identities hidden from raters. Randomized ordering. Multi-pass.
Analysis
Full report with confidence intervals, disagreement analysis, and raw data.
Where evaluation matters most.
Pre-launch safety audits
Red-team and safety scoring before a model touches production traffic.
Version regression
Catch quality drops before they ship with automated eval gating on every deploy.
Board reporting
Quarterly model-quality scorecards with leadership-ready narrative and charts.
Search ranker training
High-quality relevance labels to train and continuously improve rankers.
RAG groundedness
Human judgment of whether your RAG answers are actually supported by sources.
Speech system tuning
ASR/TTS quality measurement across accents, acoustic conditions, domains.
Common questions.
How do you calibrate human raters?
Every rater completes a scored gold set before taking live work. Agreement thresholds are project-specific — typically 85–95% with the reference set — and we drop raters below threshold until they recalibrate.
Can you run continuous evals?
Yes. We operate running eval programs with daily or weekly batches, live dashboards, and webhook-triggered reruns on model updates.
Do you publish results or can you keep everything confidential?
Your call. We've run public leaderboard evals and fully-confidential internal audits. NDAs are standard; we've never had a leak.
What if our model is multilingual?
We run evals with native-speaker raters in every target language. A 40-language safety eval typically takes 3 weeks end-to-end.
Can you compare us to competitors?
Yes. Blind head-to-head evaluations with anonymized model outputs are one of our most common project types.
How do you prevent benchmark contamination?
Custom datasets written specifically for your eval — never published, never indexed, with provenance logs that prove they never hit the training crawl.
Explore the rest of our practice.
Let's make your AI better together.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.