Compare production data to your training baseline. Get statistical drift detection, bias analysis, and audit-ready reports in under 60 seconds.
Catch non-inclusive or misrepresentative data before it trains biased models. Automatically detects protected attributes, representation gaps, and fairness issues.
Your training data was collected weeks or months ago. By the time you deploy, production data has changed: customer behavior shifted, feature distributions drifted, missing value patterns evolved. Your model trains on outdated assumptions and fails in production.
Most teams discover this after training — when the model fails in production, accuracy drops, or compliance audits reveal data quality issues. By then, you've wasted weeks of training time and thousands in compute costs.
New regulatory frameworks (EU AI Act) require proof of data quality before production deployment. Without validation, you can't prove your data meets requirements.
Compare production data to your training baseline using industry-standard statistical tests (PSI, KS-test, Chi-square). Get drift detection, bias analysis, and audit-ready reports in under 60 seconds.
Compare production data to training baseline using PSI (Population Stability Index), Kolmogorov-Smirnov test, and Chi-square test. Industry-standard methods used by banks and credit scoring systems.
Detects missing value dependencies (when column A is null, column B is always null), systematic missing patterns, and correlation analysis across columns — revealing hidden data quality issues that break ML pipelines.
Automatically detects protected attributes (gender, race, ethnicity) and flags non-inclusive or misrepresentative data. Catches representation gaps and distribution imbalances before they train biased models.
Generate audit-ready JSON reports with timestamps, all statistical test results, and EU AI Act compliance format — automatically. Manual reports take days or weeks.
Your model is only as fair as your data. earlybrd AI automatically detects protected attributes, representation gaps, and distribution imbalances that lead to biased AI systems.
Automatically identifies demographic and sensitive features (gender, race, ethnicity, age, nationality) and analyzes their distribution for fairness.
Measures how well your dataset represents diverse groups and identifies potential bias before it becomes a model problem.
Why this matters:
Biased training data creates biased models. If your dataset under-represents certain groups or has severe distribution imbalances, your model will make unfair predictions. earlybrd AI catches these issues before training, saving you from deploying discriminatory AI systems and compliance violations.
Three genuinely unique features that competitors don't offer
Detects missing value dependencies across columns (e.g., "If email is null, phone is null 90%+ of the time"). Finds systematic data collection issues that simple missing counts miss.
Great Expectations & Soda don't detect cross-column patterns
Automatically identifies target variables and applies ML-specific class imbalance thresholds (80/20 = critical, 70/30 = moderate). Understands what matters for model training.
Generic tools treat all columns the same
Identifies critical columns (ID, target, key) and weights missing values more heavily. Domain-aware penalty system that understands data importance.
Most tools treat all missing values equally
Real results from teams using earlybrd AI to catch data issues before training
"Caught data drift (PSI=0.23) before training — saved 3 weeks and $5k in compute costs."
before training failure
"Generated EU AI Act compliance report in 60 seconds. Statistical tests (PSI, KS-test) documented automatically."
per compliance audit
"Detected systematic missing pattern (column A null → column B null) that would've caused model failure. Saved weeks of debugging."
before model failure