Key results
Acquisitions integrated into a single, harmonized product catalog - with the AI-driven approach built to scale for every future acquisition, not just resolve the existing backlog.
Product records unified into one governed Enterprise Product Master, replacing fragmented, duplicate-riddled catalogs spread across the business.
Harmonized multiple ERPs into a single source of truth — giving procurement, inventory, finance, and sales teams one consistent view of the product catalog for the first time
Replaced fuzzy logic-based matching, which struggled to scale with acquisition-led growth, with AI-driven semantic matching, meaningfully improving both matching accuracy and operational efficiency across the enterprise catalog.
About the Client
A USA-based, PE-backed industrial distributor built through 25+ add-on acquisitions. Each acquisition brought its own ERP system, product catalog, and naming conventions, leaving the enterprise managing 100k+ product records with no single, trusted view of its own catalog.
Key Challenges
Acquisition-led growth outpaced fuzzy logic matching. Fuzzy string matching could no longer keep pace, leaving product data more fragmented with every new deal rather than more unified.
The same product existed under multiple identities. A single physical product frequently appeared under different SKU codes, names, and descriptions across acquired entities, with no shared enterprise product ID linking them together.
No consistent enterprise-wide view. Operating across multiple ERP systems meant procurement, inventory, and reporting teams had no reliable, unified picture of the product catalog to work from.
Manual reconciliation slowed everything down. High manual review effort was required to reconcile records, slowing catalog integration for every new acquisition and delaying the synergies the business was acquiring for in the first place.
Our Approach
Product Data Ingestion
- Pull product data from ERP systems, product catalogs, supplier files, and inventory systems across the enterprise
- Consolidate data from acquired entities into a centralized staging layer
- Normalize source formats so downstream AI processing works on a consistent data structure
AI-Powered Product Understanding
- Extract key product attributes automatically — brand, category, size, packaging, manufacturer, and other specifications
- Standardize inconsistent naming and descriptions using NLP-based interpretation
- Understand product meaning and context, not just literal text, using LLMs
Semantic Product Matching
- Match equivalent products using vector embeddings and similarity models rather than exact-text or fuzzy logic
- Apply business rules alongside AI matching to catch domain-specific edge cases
- Identify matches across different ERP systems, supplier catalogs, and acquired entities simultaneously
Confidence-Based Validation
- Assign every match a confidence score — 90%+ auto-approved, 70–90% flagged for review, below 70% routed for full review
- Route low- and medium-confidence matches to business teams for side-by-side review
- Enable a simple approve/reject/merge workflow so human judgment resolves ambiguous cases quickly
Golden Product Catalog Creation
- Consolidate validated matches into a single, de-duplicated enterprise product master
- Govern the catalog so it stays standardized, trusted, and audit-ready over time
- Make the catalog AI-ready — a reliable foundation for reporting, procurement, and inventory optimization going forward
Implemented Solution
Intelligent Product Matching
- AI-driven matching that understands product meaning and context, not just exact text
- Correctly identifies equivalent products across different ERP systems and acquired entities
- Replaces fuzzy logic-based matching that couldn't scale with acquisition growth
Confidence-Based Match Scoring
- Every match assigned a confidence score to separate high-confidence from uncertain matches
- High-confidence matches auto-approved, reducing unnecessary manual review
- Review effort concentrated only on the matches that genuinely needed a second look
Business Validation Workflow
- Human-in-the-loop review for lower-confidence matches before they enter the product master
- Simple approve/reject/merge workflow for business teams to confirm matches
- Combines AI efficiency with business judgment for maximum accuracy
Enterprise Product Master
- Single, de-duplicated, governed product master across all acquired entities
- Consistent product data feeding procurement, inventory, finance, and sales reporting
- Built to scale with future acquisitions, not just resolve the current backlog
Technologies Used
