QA, validation, benchmarking, and transcription — with documented rubrics, audit trails, and statistical significance. Your model's performance numbers should survive any peer review.
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.
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.
Every hesitation, repair, and filler word captured for conversational modeling.
Polished readable text for captions, training corpora, and content workflows.
Multi-speaker identification with timestamped speaker-change boundaries.
Word-level timestamps for TTS, dubbing, or accessibility workflows.
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.
Blind ranking of your model against competitors or internal variants.
Automated eval runs to catch quality regressions between releases.
Granular scoring across reasoning, factuality, creativity, safety, code.
Single-number dashboards with breakdowns for leadership reporting.
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.
5-point scale relevance labels with rater calibration across domains.
User intent categorization for query understanding and routing.
Faithfulness, completeness, and groundedness scoring for RAG outputs.
Pairwise preference labels to train and evaluate learning-to-rank models.
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.
Standard and weighted word error rate across accents and acoustic profiles.
Mean opinion scores for TTS naturalness and intelligibility.
Phoneme-level assessment for pronunciation training and language learning.
Expert phonetician evaluation for synthesized speech quality.
Every eval project follows a statistical protocol designed with your team — not a canned benchmark template.
Define scoring criteria, scales, and edge-case handling with your team.
Power analysis to determine dataset size for statistical significance.
Gold-set training until all raters converge on agreement thresholds.
Model identities hidden from raters. Randomized ordering. Multi-pass.
Full report with confidence intervals, disagreement analysis, and raw data.
Red-team and safety scoring before a model touches production traffic.
Catch quality drops before they ship with automated eval gating on every deploy.
Quarterly model-quality scorecards with leadership-ready narrative and charts.
High-quality relevance labels to train and continuously improve rankers.
Human judgment of whether your RAG answers are actually supported by sources.
ASR/TTS quality measurement across accents, acoustic conditions, domains.
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.
Yes. We operate running eval programs with daily or weekly batches, live dashboards, and webhook-triggered reruns on model updates.
Your call. We've run public leaderboard evals and fully-confidential internal audits. NDAs are standard; we've never had a leak.
We run evals with native-speaker raters in every target language. A 40-language safety eval typically takes 3 weeks end-to-end.
Yes. Blind head-to-head evaluations with anonymized model outputs are one of our most common project types.
Custom datasets written specifically for your eval — never published, never indexed, with provenance logs that prove they never hit the training crawl.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.