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BrainPredict Data API Reference

Complete API documentation for all 29 AI models. RESTful API with JSON request/response format, OAuth 2.0 authentication, and comprehensive error handling.

Authentication

All API requests require authentication using your API key in the Authorization header:

Authorization: Bearer bp_data_live_1234567890abcdef1234567890abcdef

Base URL

https://api.brainpredict.ai/v1

Data Quality & Cleansing

POST
/api/data/quality/assess

Assess overall data quality and get actionable recommendations

Model: Data Quality Scorer

POST
/api/data/duplicates/detect

Detect duplicate and near-duplicate records

Model: Duplicate Detector

POST
/api/data/missing/impute

Impute missing values with ML-based predictions

Model: Missing Value Imputer

POST
/api/data/outliers/detect

Detect anomalies and outliers in data

Model: Outlier Detector

POST
/api/data/validate

Validate data against business rules and constraints

Model: Data Validator

POST
/api/data/format/standardize

Standardize data formats (dates, addresses, phones)

Model: Format Standardizer

POST
/api/data/enrich

Enrich data with additional attributes

Model: Data Enricher

POST
/api/data/profile

Profile data characteristics and distributions

Model: Data Profiler

Data Rationalization

POST
/api/data/schema/harmonize

Harmonize schemas across different systems

Model: Schema Harmonizer

POST
/api/data/entities/resolve

Resolve entity identities across systems

Model: Entity Resolver

POST
/api/data/taxonomy/map

Map taxonomies and classifications

Model: Taxonomy Mapper

POST
/api/data/lineage/track

Track data lineage and transformations

Model: Data Lineage Tracker

POST
/api/data/master/manage

Manage master data and golden records

Model: Master Data Manager

POST
/api/data/reference/manage

Manage reference data and lookups

Model: Reference Data Manager

POST
/api/data/relationships/map

Map data relationships and dependencies

Model: Data Relationship Mapper

AI Readiness

POST
/api/data/ai-readiness/assess

Assess data readiness for AI/ML training

Model: AI Readiness Assessor

POST
/api/data/features/engineer

Engineer features for AI/ML models

Model: Feature Engineer

POST
/api/data/balance

Balance training data for AI/ML

Model: Data Balancer

POST
/api/data/split

Split data for training/validation/test

Model: Data Splitter

POST
/api/data/augment

Augment training data with synthetic samples

Model: Data Augmenter

POST
/api/data/optimize-for-model

Optimize data for specific AI/ML models

Model: Model Data Optimizer

Compliance & Governance

POST
/api/data/pii/detect

Detect Personal Identifiable Information

Model: PII Detector

POST
/api/data/anonymize

Anonymize sensitive data

Model: Data Anonymizer

POST
/api/data/consent/manage

Manage data processing consent

Model: Consent Manager

POST
/api/data/retention/manage

Manage data retention policies

Model: Data Retention Manager

POST
/api/data/compliance/audit

Audit compliance with regulations

Model: Compliance Auditor

POST
/api/data/access/control

Control data access and permissions

Model: Data Access Controller

POST
/api/data/lineage/audit

Audit data lineage for compliance

Model: Data Lineage Auditor

POST
/api/data/audit-trail/manage

Manage audit trails and change tracking

Model: Data Audit Trail Manager

Example: Data Quality Assessment

Request:

POST https://api.brainpredict.ai/v1/api/data/quality/assess
Authorization: Bearer bp_data_live_xxx
Content-Type: application/json

{
  "dataset_id": "clients_table",
  "columns": ["email", "phone", "address", "created_at"],
  "row_count": 150000,
  "sample_data": [
    {
      "email": "john@example.com",
      "phone": "+1234567890",
      "address": "123 Main St",
      "created_at": "2024-01-15"
    },
    {
      "email": "invalid-email",
      "phone": "123",
      "address": "",
      "created_at": "2024-02-20"
    }
  ]
}

Response:

{
  "quality_score": 72.5,
  "confidence": 96.8,
  "issues_count": 3,
  "issues": [
    {
      "type": "invalid_email",
      "count": 1250,
      "severity": "high",
      "column": "email"
    },
    {
      "type": "missing_address",
      "count": 3400,
      "severity": "medium",
      "column": "address"
    },
    {
      "type": "invalid_phone",
      "count": 890,
      "severity": "medium",
      "column": "phone"
    }
  ],
  "recommendations": [
    "Apply email validation rules",
    "Implement address enrichment",
    "Standardize phone number format"
  ],
  "estimated_fix_time": "2-3 hours",
  "model": "Data Quality Scorer",
  "model_version": "1.0.0",
  "processing_time_ms": 245
}

Error Handling

The API uses standard HTTP status codes and returns detailed error messages:

Status Codes

  • 200 OK - Request successful
  • 400 Bad Request - Invalid request parameters
  • 401 Unauthorized - Invalid or missing API key
  • 403 Forbidden - Insufficient permissions
  • 429 Too Many Requests - Rate limit exceeded
  • 500 Internal Server Error - Server error

Error Response Format

{
  "error": {
    "code": "invalid_request",
    "message": "Missing required parameter: dataset_id",
    "details": {
      "parameter": "dataset_id",
      "expected_type": "string"
    }
  }
}

Rate Limits

API rate limits vary by subscription tier:

Starter

1,000

requests/hour

Professional

5,000

requests/hour

Enterprise

20,000

requests/hour

Custom

Unlimited

contact sales

Official SDKs

Use our official SDKs for easier integration:

Python

pip install brainpredict-data

Node.js

npm install @brainpredict/data

Java

mvn install brainpredict-data

PHP

composer require brainpredict/data

Next Steps

Getting Started →

Quick start guide and first API call

AI Models →

Explore all 29 AI models

Integrations →

Connect with your data platforms