ProbeData – AI-Driven Data Validation & Reconciliation

Overview: ProbeData is a Coforge accelerator that automates the entire data validation and quality assurance lifecycle replacing manual, multi-day effort with an AI-driven platform that generates validation rules, reconciliation tests, SQL scripts, root-cause analysis, and remediation plans in minutes. Whether it is a cloud migration, data warehouse modernization, system integration QA, or compliance audit, teams today spend significant time manually interpreting mapping specifications, writing SQL test scripts, checking data quality, and diagnosing failures. ProbeData eliminates that overhead through a unified, multi-module platform delivering an 80%+ reduction in QA effort and compressing migration timelines from weeks to minutes. Part of Coforge Data Cosmos™ - the innovation backbone comprising of platforms, agents, and services that accelerates execution across every phase of the data lifecycle.

Manual Pain Points Addressed: • Weeks of manual test writing: translating mapping specs into SQL by hand • Cross-platform complexity: validating across Snowflake, Databricks, Redshift, Synapse, Fabric, PostgreSQL, MySQL simultaneously • Silent failures: schema mismatches, missing columns, empty-string placeholders undetected until production • No root-cause guidance: hours spent diagnosing WHY tests failed • Disconnected tooling: mapping specs, profiling, execution, and analysis living in separate tools

Six Integrated Modules:

1. Discovery & Data Profiling Instant schema browsing across all connected systems. Understand data before testing it.

2. Mapping Specification Validation Parses multi-sheet Excel mapping specs automatically — extracts systems, schemas, tables, column mappings, and transformation logic. Validates in strict dependency order (Systems → Schemas → Tables → Columns) with cascade blocking. Supports one-to-many, many-to-one, JOIN tables, and multi-column mappings. Reduces database round-trips by up to 90% via metadata pre-fetching.

3. Validation Rules Engine Fetches live column metadata and uses LLM to generate business validation rules: NULL_CHECK, RANGE_CHECK, UNIQUENESS, REFERENTIAL_INTEGRITY, FORMAT_CHECK, and custom rules. Each rule includes plain-English description, confidence score, priority (HIGH/MEDIUM/LOW), target column, and business rationale. Generates dialect-aware SQL for all supported platforms.

4. Data Reconciliation (Quality Engine) Production-grade data quality checks at scale — Null Check, Empty String Check, Placeholder Check (NA/N/A/UNKNOWN), Invalid Date Check. Test Suites → Test Groups → Tasks organization. Per-column pass/fail results with pass percentages and row counts. Exports as CSV or JSON.

5. AI Recommender (Failure Analysis & Remediation) LLM produces structured remediation playbooks: root cause analysis, immediate SQL fix steps, long-term preventive measures, and debug context. Severity-prioritized (CRITICAL → HIGH → MEDIUM → LOW). Full analysis history and severity dashboards.

6. Conversational AI Agent Natural language chat interface for creating validation workflows. LLM function calling browses schemas, lists tables, fetches columns, and drafts test configurations through conversation. Maintains context across sessions with intelligent summarization.

Industry Applications: • Banking: Data validation for core banking migrations to Snowflake/Redshift. Auto-generated SQL tests for BCBS 239 regulatory data reconciliation. • Insurance: Claims data reconciliation across Guidewire to Snowflake migrations. AI Recommender diagnoses actuarial data quality failures. • Travel & Hospitality: Booking data validation across GDS-to-warehouse pipelines. Reconciliation of Amadeus/Sabre feeds during peak-season loads. • Healthcare: HIPAA-compliant data reconciliation for EMR to clinical data warehouse migrations. AI-generated PHI validation rules.

Use Cases: Cloud & platform migrations, data warehouse modernization, automated data quality programs, system integration testing, compliance & audit documentation, knowledge transfer.

Cloud-Native Deployment on AWS: Deployed on Amazon EKS with containerized backend (Python 3.10+, FastAPI, Uvicorn async) and frontend (React.js, Node.js 18+). Amazon Bedrock provides LLM reasoning (GPT-4 class models). Amazon RDS PostgreSQL for auto-provisioned session-scoped processing engine. Amazon S3 for test artifacts and export storage.