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Finance & Accounting

Procurement & Supply Chain Intelligence Platform

The tool is a procurement intelligence platform that automates the three hardest parts of spend management: data ingestion, transaction categorization, and supplier normalization. The platform…

The challenge

Why it exists

Large enterprises generate spend data across dozens of disconnected systems — ERPs, AP platforms, spreadsheets — with inconsistent supplier names, unstructured transaction descriptions, and no standardized transaction categorization. Without clean & classified transactions - the procurement leaders cannot identify where money is going, consolidate fragmented supplier relationships, or spot savings opportunities that typically range from 5–15% of total spend.

The approach

How it works

The tool is a procurement intelligence platform that automates the three hardest parts of spend management: data ingestion, transaction categorization, and supplier normalization. The platform connects to enterprise data sources — ERPs like SAP and Oracle, financial systems, data warehouses, or simple file uploads — through a unified connectors hub. Once data is ingested, an in-house machine learning engine classifies raw transactions against a hierarchical spend taxonomy using vector embeddings and clustering algorithm. Analysts review AI-generated clusters and approve category assignments, which then train the system to auto-classify future transactions via vector similarity search — a human-in-the-loop approach that improves accuracy over time. For supplier management, the platform normalizes inconsistent vendor names into unified groups and provides Pareto analysis, segmentation, and multi-factor risk scoring. Interactive dashboards surface maverick spend, tail spend, consolidation opportunities, and contract renewal alerts — turning messy procurement data into actionable savings insights.

Key capabilities

What it does

The tool is a procurement intelligence platform that automates the three hardest parts of spend management: data ingestion, transaction categorization, and supplier normalization.

The platform connects to enterprise data sources — ERPs like SAP and Oracle, financial systems, data warehouses, or simple file uploads — through a unified connectors hub.

Once data is ingested, an in-house machine learning engine classifies raw transactions against a hierarchical spend taxonomy using vector embeddings and clustering algorithm.

Analysts review AI-generated clusters and approve category assignments, which then train the system to auto-classify future transactions via vector similarity search — a human-in-the-loop approach that improves accuracy over time.

Typically used by

Chief Procurement Officers, Category Managers, and Strategic Sourcing teams at mid-to-large enterprises managing complex, multi-supplier spend portfolios across business units.

Business impact

80–90% reduction in manual spend classification time — from weeks of analyst effort to hours with human-in-the-loop AI

- 80–90% reduction in manual spend classification time — from weeks of analyst effort to hours with human-in-the-loop AI. - 5 to 15% addressable savings identification through maverick spend detection, tail spend analysis, and supplier consolidation insights. - Supplier deduplication eliminates fragmented vendor records, enabling accurate spend-per-supplier visibility and stronger negotiation leverage. - Continuous improvement — the classification engine gets smarter with every analyst approval, reducing manual intervention over time. - Contract risk reduction through automated expiration alerts and renewal tracking.

Built with

Technology

Tools & Frameworks

Frontend: Next.js 15React 19Tailwind CSSRadix UIRechartsAG Grid | Backend: NestJSPrisma ORMPostgreSQL 17Redis 7sentence-transformersTurborepo monorepo

Integrations

SAP S/4HANAOracle Fusion CloudMicrosoft Dynamics 365Dun & BradstreetEcoVadisRapidRatingsSnowflakeGoogle BigQueryGoogle Sheets

Want something like this for your team?

We'll map your workflow and scope a working prototype — typically in three weeks, not three months.

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