Data Quality Analyzer

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.

Start BuildingView Documentation

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. Suggests optimal transformations for common ML frameworks.

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 required.

API Endpoints

Production-ready REST API endpoints. All requests require a valid API key in the Authorization header.

POST
/api/v1/dataready/analyze

Submit a dataset or data sample for comprehensive quality analysis. Returns quality scores across all dimensions, detected anomalies, schema issues, and prioritized improvement recommendations.

POST
/api/v1/dataready/score

Get a quick AI-readiness score for a dataset. Faster than full analysis, returns an overall readiness score and top-3 blocking issues that need to be resolved.

Example Request

curl
curl -X POST \
  https://api.bolor.ai/api/v1/dataready/analyze \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "Your input here",
  "options": {
    "max_latency_ms": 5000,
    "min_confidence": 0.8
  }
}'

Use Cases

See how teams are using DataReady in production today.

01

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, saving GPU hours and preventing model degradation.

02

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.

03

ETL Quality Gates

DataReady integrates into ETL pipelines as a quality gate between stages. Data that fails quality checks is quarantined for review rather than propagated, preventing bad data from contaminating production systems.

Start Building with DataReady

Get your API key and make your first call in under 5 minutes. Free tier includes 100 requests per hour.

Get Started FreeRead the Docs