Specialized testing engagements for AI-powered products, built around your stack, your users, and your risk.
Structured, repeatable assessment of your model's outputs so you know exactly where it performs well, where it doesn't, and what to fix before users find out.
Measure how often your model fabricates facts, cites wrong sources, or answers questions it shouldn't. Scored against your specific domain, not generic benchmarks.
We define what "good" means for your use case, whether that's summarisation, Q&A, classification, or code gen, and build test suites that measure it consistently.
Outputs that are technically accurate but rambling or off-topic still fail users. We evaluate response quality end-to-end, not just factual correctness.
Planning to switch models or upgrade to a newer version? We run head-to-head evaluations so you can see exactly what changes before you ship it.
Systematic exploration of inputs your model handles poorly: ambiguous queries, rare domains, adversarial phrasing, and long-tail scenarios your users will eventually hit.
A clear, prioritised report with failure examples, severity ratings, and specific recommendations. Not just a score, but a plan to act on.
You're approaching launch and need confidence in what the model actually does under real conditions.
You've changed models, updated your prompts, or added new features and need to verify nothing regressed.
Users are complaining about output quality but you don't have a systematic way to reproduce or measure the problem.
You need to demonstrate reliability to enterprise customers, investors, or compliance teams.
A focused evaluation sprint, typically 1 to 2 weeks. Covers accuracy, coherence, edge cases, and a prioritised findings report.
We design and build a repeatable evaluation harness your team owns. Includes test suites, scoring rubrics, and documentation.
Continuous evaluation across model updates, prompt changes, and new feature releases. You get a dedicated testing expert without the full-time hire.
Rigorous testing for machine learning models in production. From data quality through to bias analysis and explainability, covering the dimensions that standard QA misses entirely.
Automated and manual checks to identify data quality issues before they become model problems. Includes categorising data into relevant sub-classes for targeted analysis.
Synthetic data generation and adversarial testing to verify your model handles unexpected deviations and real-world failure scenarios without silently degrading.
Detailed analysis using F1 Score, Precision, Recall, AUC, RMSE, and similar metrics relevant to your model type. Goes beyond accuracy to give you a nuanced performance picture.
Identification of biases across gender, race, and data distribution dimensions, with actionable mitigation recommendations your team can act on before deployment.
Using tools like SHAP, LIME, and PDP to create visualisations that explain model decision-making at both global and local levels. Useful for compliance, audits, and stakeholder trust.
Baseline comparisons and version-to-version analysis to track progress across training runs, fine-tunes, or architecture changes and ensure continuous improvement.
Your model is performing below expectations in production and you need a systematic diagnosis, not just intuition.
You're in a regulated domain and need documented evidence of fairness, explainability, or performance thresholds.
You're retraining or updating your model and need confidence that the new version is genuinely better, not just different.
Your ML team needs an independent testing perspective to catch what internal familiarity with the model tends to miss.
A structured review covering data quality, metrics, bias, and explainability. Delivers a prioritised findings report your team can act on immediately.
An in-depth engagement across all six testing dimensions, typically structured over several weeks with phased delivery of findings and recommendations.
Continuous testing across model updates and data changes. Useful for teams that retrain frequently and need a consistent evaluation baseline.
Tell me what you're building. We'll figure out the right scope together.
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