DataReady
Know exactly how ready your data is for AI before you build. DataReady scores your datasets on completeness, consistency, accuracy, and bias, then provides actionable recommendations to improve data quality. Stop training models on bad data and wasting compute on datasets that are not ready.
Key Features
Everything you need to integrate DataReady into your production systems.
Quality Scoring
Comprehensive quality scores across six dimensions: completeness, consistency, accuracy, timeliness, uniqueness, and validity. Each dimension gets a 0–100 score with detailed breakdowns.
Anomaly Detection
Automatically detect outliers, distribution shifts, missing value patterns, and data corruption. Statistical and ML-based detection methods identify issues that manual review would miss.
Schema Analysis
Analyze data schemas for AI readiness. Detect type mismatches, encoding issues, cardinality problems, and feature engineering opportunities.
Readiness Assessment
Get a comprehensive AI-readiness score that predicts how well your data will perform for specific ML tasks. Includes recommendations prioritized by impact and effort.
API Reference
Production-ready REST API. All requests require a valid API key via Authorization header.
/api/v1/dataready/analyzeSubmit a dataset or sample for comprehensive quality analysis. Returns quality scores, detected anomalies, schema issues, and prioritized improvement recommendations.
/api/v1/dataready/scoreGet a quick AI-readiness score for a dataset. Returns an overall readiness score and top blocking issues that need to be resolved.
curl -X POST https://api.bolorintelligence.com/api/v1/dataready/analyze \
-H "Authorization: Bearer bolor_sk_..." \
-H "Content-Type: application/json" \
-d '{
"data_description": "Customer transactions for churn prediction model",
"sample_data": [
{"user_id": "u_123", "amount": 49.99, "date": "2026-01-15"},
{"user_id": "u_456", "amount": null, "date": "2026-01-16"}
],
"use_case": "churn_prediction"
}'Use Cases
See how teams are using DataReady in production today.
ML Pipeline Preparation
Data science teams run DataReady before every training cycle to validate that incoming data meets quality thresholds. Automated quality gates prevent training on corrupted or biased data.
Data Warehouse Auditing
Data engineering teams use DataReady to continuously monitor data warehouse quality. Scheduled scans detect drift, corruption, and quality degradation before downstream consumers are affected.
ETL Quality Gates
DataReady integrates into ETL pipelines as a quality gate. Data that fails quality checks is quarantined for review rather than propagated, preventing bad data from contaminating production systems.
Explore More Products
DataReady works even better with the rest of the platform.
Start Building with DataReady
Get your API key and make your first call in under 5 minutes. Free tier includes 100 API calls per month.