Deployed multi-agent procurement system that autonomously sources, compares, and orders industrial parts across 340+ suppliers — cutting procurement cycle time by 73% and saving ₹1.8Cr annually.
Client
Industrial Parts Distributor
-73%
Cycle Time
₹1.8Cr
Annual Savings
-82%
Manual Processing
-61%
Stockout Rate
An industrial parts distributor managing 47,000+ SKUs across 340 suppliers was drowning in manual procurement. Their six-person purchasing team spent 70% of their time on routine reorders — checking inventory levels, comparing prices across supplier catalogs (most of which were PDF-only or emailed Excel sheets), negotiating MOQs, tracking delivery windows, and managing substitutions when parts were out of stock. Average procurement cycle for a standard reorder was 4.2 days. For non-standard or urgent parts, it stretched to 11+ days. McKinsey's January 2026 research on agentic commerce identified this exact pattern: procurement in B2B operates at Level 0-1 on the automation curve — rules-based convenience and basic assistance — while the technology exists to push it to Level 3-4 with supervised execution and autonomous intent management. The distributor's competitors were already piloting AI-assisted sourcing, and the company risked losing key accounts to faster suppliers.
We built a multi-agent procurement system with four specialized AI agents coordinated through a central orchestrator. The Inventory Agent monitored real-time stock levels across 3 warehouses and triggered procurement workflows based on dynamic reorder points (not static thresholds) that factored in seasonal demand patterns, lead times, and sales velocity. The Sourcing Agent ingested supplier catalogs — including OCR processing of PDF price lists and automated extraction from emailed Excel sheets — into a normalized product database, enabling real-time price and availability comparison across all 340 suppliers. The Negotiation Agent handled routine quote requests, applied pre-negotiated volume discounts, identified substitution opportunities when preferred parts were unavailable, and escalated to human buyers only for orders above ₹5L or non-standard terms. The Compliance Agent verified every purchase against approved vendor lists, checked certifications (ISO, RoHS, REACH), and ensured adherence to procurement policies. The system used n8n for workflow orchestration, OpenAI o4-mini for reasoning, and CrewAI for multi-agent coordination. All agents operated within strict authorization boundaries — the purchasing team set budget limits, supplier preferences, and substitution rules, and the system executed within those guardrails while surfacing exceptions for human review.
-73%
Cycle Time
Procurement cycle reduced from 4.2 days to 1.1 days
₹1.8Cr
Annual Savings
Cost reduction from optimized sourcing and volume consolidation
-82%
Manual Processing
Reduction in routine procurement tasks
-61%
Stockout Rate
Fewer stockouts from predictive reordering
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