Fairy for Data Science.
AI built the model. Fairy makes sure it actually works.
Pipeline correctness, statistical validity, bias detection, and drift risk — reviewed by a senior data scientist before your model reaches production.
The platform
AI + Fairy = production-ready models.
AI tools can build a working model in hours. They cannot tell you whether that model will hold up under real data distributions, satisfy regulatory requirements, or perform equitably across your user population.
Fairy is the operating layer between AI output and production deployment. Senior data scientists verify what automated tools miss. The platform continuously monitors models in production and catches drift before it causes damage.
What we verify
- Data pipeline correctness and leakage detection
- Train/test contamination and data splitting validity
- Statistical assumptions and significance testing
- Model bias and fairness across demographic segments
- Feature engineering correctness
- Overfitting and generalization risk
- Concept drift and monitoring setup
- Deployment readiness and inference correctness
- Reproducibility and documentation completeness
- Regulatory compliance (GDPR, CCPA, FCRA, ECOA)
What's at risk
Accuracy metrics don't catch everything.
A model can score well in testing and fail in production. These are the failure modes metrics don't surface.
Flawed model in production
A model with data leakage inflates performance in testing and fails silently in production.
Biased outcomes
Disparate impact against protected groups creates legal liability and reputational harm.
Silent data corruption
A feature engineering bug corrupts predictions without triggering any alerts.
Model drift undetected
A model degrades as data distributions shift — without a monitoring setup, no one notices until damage is done.
How it works
From AI model to production confidence.
Submit your pipeline
Share your model, training code, and evaluation results. Describe the production use case and any regulatory context. We match you with a senior data scientist in your domain.
Fairy verifies and monitors
A senior data scientist reviews the full pipeline — not just the model card. Software analysis runs in parallel. Findings include severity, root cause, and remediation guidance.
Verified verdict, continuously
A signed verdict, structured findings, and ongoing drift monitoring. The platform learns your data distributions and alerts when something changes.
FAQ
Common questions.
What does Fairy verify in AI-generated data science work?
Fairy verifies pipeline correctness (data leakage, train/test contamination), statistical validity (assumptions, significance, sample size), model assumptions (linearity, independence, normality where required), bias and fairness across demographic segments, feature engineering correctness, and deployment readiness including drift monitoring setup.
Why can't I just check model accuracy metrics?
Accuracy metrics only tell you how well a model performed on test data. They don't reveal data leakage (which inflates metrics), concept drift (the model will degrade over time), feature engineering errors, bias against protected groups, or whether the model's assumptions hold in your specific production environment. These issues only show up in production — often after significant damage has been done.
What kinds of models does Fairy review?
Fairy reviews classification, regression, clustering, recommendation, time-series forecasting, NLP, and LLM-based pipelines. Our senior data scientists have experience across industries including finance, healthcare, e-commerce, and enterprise SaaS.
Does Fairy check for AI model bias?
Yes. Fairy explicitly checks for disparate impact across demographic segments, proxy variable use, training data representativeness, and whether bias mitigation techniques are appropriate and correctly implemented. This is especially important for models used in hiring, lending, healthcare, or any high-stakes decision context.
How is my data handled during review?
Reviewers access only what is necessary to evaluate the model — typically code, schema, and sample data (never full production datasets unless required). All data is handled under strict confidentiality. We support review of de-identified or synthetic sample data as an alternative to production data access.
Can Fairy help after a model is already in production?
Yes. Fairy offers both pre-deployment verification and ongoing monitoring for models already in production. This includes drift detection, periodic re-evaluation as data distributions shift, and incident response when a model behaves unexpectedly.