OIL & GAS — PROCUREMENT & SUPPLY CHAIN

AI-Powered RFQ Automation & Supplier Sourcing

A Singapore-based supply chain company serving the oil and gas industry receives a constant stream of inbound requests for quotation (RFQs). Each RFQ carries several line items, and different items are sourced from different suppliers. The purchase team reads every RFQ by hand, works out which supplier can supply which item, and drafts each outbound supplier email manually — slow, repetitive work that depends entirely on each person's memory of who supplies what. On top of that, a catalogue of 10,000+ historical product records held inconsistent, free-text descriptions that were almost impossible to search or match against suppliers.

~20 hrs

Saved per week

5x

Faster RFQ drafting

90%

Sourcing match accuracy

100%

Supplier knowledge captured

Automated Workflow

How the automation handles the entire process end-to-end

HIGHLOW / UNMATCHEDRFQ revision re-entersOutlookMailboxes(procurement desks)ReadMailboxValidate Ref IDformat + duplicateSplit intoLine ItemsAI Sourcing+ ConfidenceClaude + vector searchCONFIDENT?Draft RFQsby supplier(Outlook Drafts)Team Reviews& SendsSent ✓Hold forReviewProcurement Deskflags exceptionsPurchase Teamconfirm / add supplier/ add productSupabase — PostgreSQL + pgvectorsupplier-product master (vector embeddings) · RFQ threadsdrafted / sent emails · confidence scores · audit logLEGENDMain flowConfident pathReview / exceptionIteration loopHuman-in-the-loopData read/writeDecision PointAnimated dots show data flowing through each path

This workflow is for demonstration purposes only. We custom-build automation architectures tailored to each client's specific requirements, tools, and processes.

How It Works

Each step in the automation pipeline

01

Historical Data Standardisation

Claude reads 10,000+ free-text product descriptions and generates clean, standardised generic product names, creating a reliable supplier-product baseline in Supabase.

02

Vector Embeddings for Every Product

Each standardised product is embedded and stored in a Supabase pgvector column, so line items can be matched by meaning, not just exact text.

03

Mailbox Reading & Intake Validation

The system reads the procurement desk Outlook mailboxes, picks up inbound RFQs by their reference ID, and validates format and duplicates before anything is processed.

04

Line-Item Split & AI Sourcing

Each RFQ is broken into line items; exact part numbers are matched first, otherwise pgvector similarity search finds the most likely suppliers from the master table.

05

Confidence-Scored Matching

Every supplier-to-product match gets a High / Medium / Low score; confident matches are drafted automatically, uncertain ones are held for a human.

06

Grouped Draft Preparation

Line items are grouped by supplier and a clear outbound RFQ draft is prepared per supplier in the desk mailbox, carrying the correct company-entity signature.

07

Human-in-the-Loop Review

The purchase team reviews every draft and sends it themselves; nothing leaves automatically. Status moves from "drafted" to "sent".

08

Self-Improving Database

Corrections, new suppliers and manually added products flow back into Supabase and are re-embedded as vectors, so matching gets steadily more accurate over time.

Before & After

The measurable impact of automation

Before Automation
RFQ drafting2-3 hours per RFQ
Supplier knowledgeLocked in people's heads
Product catalogue10,000+ inconsistent rows
Sourcing methodManual recall
New-hire ramp-upWeeks
Audit trailScattered across inboxes
After-hours draftingNone
After Automation
RFQ draftingMinutes (drafts ready to review)
Supplier knowledgeCaptured in the database
Product catalogueStandardised + vector-indexed
Sourcing methodAI + vector similarity search
New-hire ramp-upDays
Audit trailComplete and searchable
After-hours drafting24/7

Tech Stack

Tools and technologies powering this automation

Claude (Anthropic)Make.comSupabase (PostgreSQL)pgvector — Vector SearchVector EmbeddingsMicrosoft 365 / Outlook APIRailwayGitHubMCP Servers

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