ProcuLens.

The problem

Manufacturing procurement teams spend significant time comparing supplier quotes for indirect materials. After collecting quotes, staff manually enter each one into Excel and pick a supplier on price, delivery, and past relationship. Market-price research is done when possible, but most people skip it.

Indirect materials are often deprioritized versus direct materials, so each site buys as needed and cost optimization suffers. For teams managing multiple factories and 1,000-5,000 SKUs, comparison work is a heavy, know-how-dependent burden — and negotiation, which should follow from history and market prices, usually doesn't happen at all.

The solution

ProcuLens is an LLM-powered supplier recommendation assistant that goes beyond lowest price. It extracts and normalizes quote data, combines it with quantitative and qualitative purchase history plus market-price references, and recommends a supplier grounded in evidence.

The explicit objective is not simply the cheapest supplier. It separates quote information, transaction history, qualitative feedback, and market prices, then explains in practical terms why a supplier is recommended. Users can review and edit extracted data, inspect the supporting evidence, and see suggested next actions such as price negotiation or delivery confirmation — treating market prices as reference for validation, never the primary basis.

How it works

The user uploads quote files (PDF, Excel, image) and enters the target material. The system extracts quote data via OCR and LLM parsing, normalizes units, dates, currencies, and model numbers, and validates that fields are complete and totals match. After the user reviews the results, LLM function calling retrieves context three ways: SQL search on Supabase for quantitative history, semantic search (RAG) for qualitative notes like quality issues and delivery delays, and a Tavily web search for market prices.

Retrieval uses OpenAI text-embedding-3-small with pgvector. The model runs at low temperature and returns strict JSON, keeping each source clearly separated and writing "Not retrieved" or "No relevant information found" when data is missing. Across 10 evaluated runs it scored 10/10 on recommendation reasoning and next-action clarity, 9/10 on missing-data handling, 8/10 on non-hallucination, and 7/10 on source citation; Claude Sonnet 4.6 and Haiku 4.5 both produced accurate, easy-to-read decisions.

Who it's for

The users are procurement staff at manufacturing companies who collect indirect-material quotes from multiple suppliers — especially people managing many factories and SKUs who spend significant time on comparison work.

Customers are B2B small and mid-sized manufacturers handling roughly 1,000-5,000 SKUs of indirect materials and MRO supplies. The delivering business provides FDE-style AI and DX implementation support, differentiating by helping SMEs design and implement targeted AI workflows quickly and at low cost rather than deploying a large core system.

Why it matters

Rising raw-material, logistics, and labor costs have made procurement cost optimization increasingly important, while finance teams are asked to do more with the same headcount. The procurement software market is forecast to grow around 10-12.5% annually, and procurement analytics in Japan at a 25.7% CAGR through 2030.

ProcuLens targets operational efficiency, less dependence on individual know-how, and standardized processes across sites — the gap left by tools like Coupa and SAP Ariba. It launches as a limited pilot with a small team of procurement staff to validate the workflow from upload through extraction, comparison, recommendation, and email drafting, with success measured by time saved per comparison and estimated cost reduction.

At a glance

Project

ProcuLens

Built by

Mifuyu Fujii

One-liner

ProcuLens is an LLM-powered tool for manufacturing procurement teams making indirect-material supplier decisions after collecting quotes.