AI Spend Categorization
SupplyHub
Transforming spend chaos into crystal-clear cost intelligence with AI-driven categorization.
Overview
Project Overview
SupplyHub's enterprise clients were drowning in unstructured spend data — millions of transactions across disparate ERP systems, credit card platforms, and invoice databases with inconsistent vendor naming, misclassified categories, and no unified taxonomy.
AgilizTech engineered an AI-driven spend intelligence engine that auto-categorizes transactions, standardizes vendor records, and reveals actionable cost insights across all systems in real time — processing what previously took weeks in minutes.
Industry
Procurement & Finance
Timeline
10 weeks
Tech Stack
The Challenge
What problems needed solving?
Unstructured Transaction Data
Over 2 million monthly transactions arrived in non-standardized formats with free-text descriptions, making automated categorization nearly impossible.
Vendor Name Inconsistency
The same supplier appeared under 15-20 different naming variations across systems, preventing accurate spend consolidation and negotiation leverage.
Manual Classification Backlog
A team of 8 analysts spent 70% of their time manually categorizing and reclassifying spend — a process that was 3 months behind at any given time.
Missed Savings Opportunities
Without accurate spend visibility, the organization was unable to identify $5M+ in consolidation, maverick spending, and contract leakage opportunities.
The Solution
How AgilizTech delivered results
Intelligent Classification Pipeline
Built a multi-stage NLP pipeline that analyzes transaction descriptions, vendor records, and contextual metadata to classify spend into a 4-level UNSPSC taxonomy with 97.3% accuracy.
AI Entity Resolution Engine
Developed a fuzzy matching and entity resolution system that identifies and merges duplicate vendor records across all source systems into a single golden record.
Real-Time Spend Dashboard
Created an interactive analytics layer that surfaces spend patterns, anomalies, and savings opportunities as transactions are processed — not weeks later.
Continuous Learning Loop
Implemented a feedback mechanism where analyst corrections automatically retrain the model, improving accuracy from 89% to 97.3% within the first 6 weeks.
The Benefits
Measurable business impact
97.3%
Accuracy
Auto-categorization accuracy, up from 62% with rule-based approaches.
50x
Speed
Faster processing — 2M transactions categorized in minutes instead of weeks.
$8.1M
Savings Found
In addressable savings identified through consolidated spend visibility in the first quarter.
80%
Analyst Efficiency
Reduction in manual classification effort, redirecting team to strategic work.
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