CASE STUDY · AI MDM PLATFORM

AI-Powered Master Data
Management Platform

An end-to-end intelligent data platform that ingests, profiles, cleanses, enriches, and governs master data - replacing weeks of manual cleaning with automated AI pipelines.

80%Less Manual Cleaning
7AI Feature Modules
RAGPowered Enrichment

The Problem

Organizations struggle with fragmented, low-quality master data spread across dozens of sources

Fragmented Data Sources

Master data spread across CSV files and Excel sheets with no centralized system to ingest or manage it.

Undetected Duplicates

Near-duplicate records and inconsistent formats go undetected without automated deduplication tooling.

Excessive Manual Effort

Teams spend excessive time on manual data cleaning, validation and formatting — reducing productivity.

No Enrichment Capability

No automated way to fill missing field values or standardize formats across large datasets.

Hard-to-Maintain Pipelines

Data transformation pipelines built through manual coding are complex, fragile, and hard to reuse.

Poor Downstream Quality

Downstream analytics and decision-making suffer from unreliable, low-quality master data.

The Solution

A comprehensive AI-powered MDM platform covering every step from raw ingestion to trusted master data

Before: The Data Chaos

Organizations deal with large volumes of master data spread across multiple sources - CSV files, Excel sheets - rife with duplicates, inconsistent formats, and missing values that go undetected. Manual cleaning cycles are expensive, error-prone, and impossible to scale.

After: Intelligent Data Mastery

The AI MDM platform automates data ingestion, profiling, deduplication, cleansing, enrichment, and pipeline building. AI engines powered by OpenAI GPT, RAG retrieval, fuzzy matching, and TF-IDF cosine similarity deliver reliable, governed master data for downstream analytics.

Manual Approach
Weeks

Manual deduplication, rule writing, and pipeline coding per dataset

AI MDM Platform
Minutes

AI-driven profiling, deduplication and enrichment with human review

80%Effort Saved

Platform Feature Modules

Seven purpose-built AI modules covering every stage of the master data lifecycle

01

Dataset Ingestion & Management

  • Multi-format upload (CSV & Excel) with client-side parsing via PapaParse for instant preview.
  • Chunked streaming upload in 500-row batches with Server-Sent Events (SSE) for live progress.
  • Interactive column mapping, full CRUD operations, and CSV export for previews & duplicate rows.
02

Data Profiling & Exploration

  • Automatic column-level statistical profiling: null count, unique count, type detection, and duplicate flags.
  • Visual quality bars showing completeness vs. uniqueness per column with per-column warning indicators.
  • Dynamic paginated tabular preview using Oracle JSON_VALUE for efficient navigation of large datasets.
03

AI Recommendations & Data Quality

  • Fuzzy duplicate detection (RapidFuzz) + TF-IDF cosine similarity clustering for semantic deduplication.
  • Format inconsistency detection, unit standardization (lbs→kg), and canonical golden-value suggestion.
  • Cross-column business rule validation with cluster review panel and bulk apply/export for offline review.
04

Data Cleansing Engine

  • Configurable rule-based cleansing with AI-assisted deduplication using survivorship strategies (manual, most-frequent, AI-driven).
  • Missing value imputation (mode / manual / AI-driven), whitespace normalization, and special-character fixing.
  • Human-in-the-loop review queue: approve, reject, skip, or auto-resolve — with full session history & snapshot downloads.
05

Data Enrichment (GPT + RAG)

  • OpenAI GPT extracts missing field values from source descriptions with RAG-based example retrieval for context.
  • Local pattern-matching fallback when the OpenAI API is unavailable, ensuring zero-downtime enrichment.
  • Configurable source-to-target mappings, valid-value enforcement, and real-time SSE progress during batch runs.
06

Visual Dataflow Builder

  • Drag-and-drop pipeline builder (ReactFlow) with nodes: Dataset, Filter, Join, Aggregate, Transform, Split, AI Normalize, Output.
  • SQL-based execution engine that compiles all transformations to Oracle SQL via temp tables with topological sorting.
  • Preview mode for flow validation, execution history with row counts & timing, and result-dataset creation from output.
07

Master Data Insights

  • Consolidated cross-dataset quality overview with composite quality scoring across multiple dimensions.
  • Drill-down insight detail pages by type with granular analysis for targeted data governance.
  • Role-based access control (Admin, Editor, Viewer) with secure JWT authentication and Oracle DB backend.

Project Objectives

Eight core objectives that drove the design and architecture of the AI MDM platform

Centralize data management - single platform for ingest, profile, cleanse, and analyze from multiple sources.

Automate data quality - AI detects and resolves duplicates, inconsistencies, and missing values.

Intelligent data profiling - column-level statistics: completeness, uniqueness, type detection, and quality scoring.

AI-driven cleansing - configurable rules, golden record creation, and human-in-the-loop workflows.

Enrich incomplete data - LLM + RAG extraction from source descriptions fills missing field values.

Reusable pipelines - visual dataflow builder to create, save, and execute transformations on demand.

Explainable AI - every recommendation is transparent with affected-row previews and manual override.

Data governance - RBAC (Admin/Editor/Viewer), JWT auth, and enterprise Oracle database backend.

Technology Stack

Built with proven enterprise-grade technologies and cutting-edge AI APIs

React + ReactFlow
FastAPI
Python / RapidFuzz / sklearn
Oracle DB
AI/LLM/RAG
SSE Streaming

Business Value Delivered

Measurable improvements across data quality, efficiency, and governance

Drastic Time Savings

AI-automated deduplication, profiling, and enrichment eliminates weeks of manual data preparation for each dataset.

80%Reduction in Manual Effort

Higher Data Quality

Multi-dimensional quality scoring, fuzzy deduplication, and AI normalization produce reliable master data for analytics.

5xImprovement in Data Accuracy

Reusable Pipelines

Visual drag-and-drop dataflow builder lets teams create, save, and reuse transformation pipelines without any coding.

10xFaster Pipeline Creation

Ready to Transform Your Data Quality?

Let us build an AI-powered MDM platform tailored to your data ecosystem - from ingestion to trusted, governed master data in record time.