Every incoming RFQ costs your team 15–45 minutes of manual research — reading specs, cross-referencing vendor catalogs, pulling pricing history, building the comparison. We build custom RFQ engines that do that research automatically and hand your buyer a ranked recommendation to approve.
RFQ processing is the bottleneck nobody budgets for. Volume climbs, response times slip, and buyers start losing deals to whoever quoted first — not whoever quoted best.
Four stages, each doing one job. A human approves at the end — the engine never awards a contract on its own.
Pull structured requirements — product type, quantity, specs, tolerances, deadline — out of unstructured RFQ text, wherever it arrives: email body, PDF attachment, web form, spreadsheet.
Score and rank suppliers against the parsed requirement using your catalog and historical performance data — fill rate, lead time, quality history — not just who's alphabetically first.
Pull quote history and market benchmarks so the numbers arrive with context: what this part cost last quarter, what this vendor usually quotes, whether the bid is in range.
Produce a ranked vendor comparison with the reasoning attached, ready for a buyer to review and award in minutes instead of assembling it from scratch.
The staging matters more than the model choice. Each stage fails differently and gets measured differently — which is what makes the engine debuggable when a quote comes out wrong. Read the reasoning in how many AI agents your product actually needs →
A B2B procurement team was handling dozens of RFQs a day entirely by hand. Volume was growing, headcount wasn't, and response times were costing deals.
"We needed help with full automation and data analytics. Glissando AI has created an AI search tool that does most of the research needed for proposals. Glissando AI's work has improved our time savings, efficiency, and accuracy."
Machine parts, tooling, and materials RFQs where specs arrive in inconsistent formats and matching depends on real tolerances, not keyword overlap.
Distributors fielding high daily quote volume across a large SKU catalog, where speed of response decides who wins the order.
Groups running several business units or regions, where each entity has its own vendor list, pricing agreements, and approval rules on one shared pipeline.
Where RFQs arrive as free-text emails with spreadsheet or PDF attachments and someone currently retypes them into a system by hand.
Any process where the gap between request received and vendor awarded is measured in days and could be measured in hours.
Manufacturer and supplier websites where inbound quote requests sit in an inbox until someone has time to research them.
We build the intake, storage, and processing layer before any AI ranking goes in — so you stop losing quote and vendor data while the system is still being built, and so the model has real history to learn from when it arrives. Activation comes after the data is clean and flowing, not before.
You own everything at the end: code, data, and infrastructure, from day one. No licensing-back, no retained rights.
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