AI Model Validation
Structured human evaluation of what your model actually produces — scored against a rubric, with the disagreements visible rather than averaged away.
What is included
AI Model Validation, in detail
Rubric design
Clear, testable criteria so two reviewers reach the same score for the same reason.
Blind evaluation
Reviewers scoring without knowing which model or version produced the output.
Side-by-side comparison
Head-to-head evaluation between model versions or vendors.
Failure taxonomy
Errors categorised so you know what kind of wrong the model is, not just how often.
Reproducible reporting
Scores, agreement statistics and the raw judgements handed over with the report.
How we work
Four steps, no surprises
- ConsultationWe learn the business and what success looks like.
- Audit & scopeA written plan: what we build, in what order, at what cost.
- BuildDelivered in stages you review as we go.
- SupportMonitoring and iteration once it is live.
Questions
Frequently asked
Can you evaluate safety behaviour?
Yes — refusal quality, harmful-content handling and jailbreak resistance, against a rubric you approve.
How large should an eval set be?
Large enough to separate the models you are comparing. We size it from the effect you need to detect.
More Data services
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Sourcing and assembling training datasets to a defined specification, with provenance tracked and licensing documented.
Learn more →Data Preprocessing
Cleaning, normalising, deduplicating and structuring raw data so it is genuinely fit to train on.
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Text, image, audio and video annotation against your guidelines, with multi-pass QA and measured agreement.
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Instruction, preference and evaluation datasets built and reviewed by humans for fine-tuning and alignment work.
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Emissions, energy and resource-use data gathered from disclosures and standardised across entities for comparison.
Learn more →Social Data Research
Workforce, community and supply-chain indicators researched from disclosed sources and structured for ESG scoring.
Learn more →