AI RFQ product matching helps teams shortlist catalogue items. See how Singapore SMEs can check specifications and resolve uncertainty before quoting.
A customer asks for a replacement part using an old code, a short description and a photo. Your catalogue uses a different naming convention. The salesperson now has to work out what the customer means before anyone can prepare a sensible quotation.
For distributors and engineering SMEs, a request for quotation (RFQ) is often a translation problem between customer language and internal product data. AI can assist that search, but a plausible suggestion must not be mistaken for an approved substitute.
Start by separating the request into fields
Keep the original email or attachment, then extract each requested item into its own row. Record the wording, manufacturer or model if supplied, quantity, unit of measure and required specifications. Mark missing information as unknown rather than filling it with a likely value.
This preparation is the role of document extraction and validation. A clean search cannot repair a misread model number. The reviewer should be able to return to the relevant page or email line without searching through the whole enquiry again.
Use exact codes before broader product suggestions
A useful matching workflow has several routes, each with a different purpose:
- Exact identifiers: look for a complete manufacturer part number or internal stock code. Preserve significant punctuation, suffixes and revisions; do not assume that similar codes identify the same product.
- Approved aliases: use maintained mappings for old codes, customer-specific descriptions and supplier references. Record who approved a mapping and when it should be reviewed.
- Descriptive search: use keywords and meaning-based search to retrieve candidates when identifiers are absent. Apply known requirements as filters, then show unresolved differences.
Microsoft’s hybrid-search documentation explains why combining keyword and meaning-based search can be useful: keyword matching suits precise identifiers such as product codes, while vector search can retrieve conceptually similar wording. That is a retrieval approach, not proof that two engineering products are interchangeable.
An incomplete specification should produce a question
Consider an illustrative RFQ: “20 stainless steel brackets, 50 mm, same as the previous order.” In an invented catalogue, BR-50-A and BR-50-B share that description but have different hole patterns. Neither the short description nor a high similarity score resolves which one the customer needs.
The useful output is a two-item shortlist with the hole-pattern difference highlighted and a request for the previous order reference or drawing. If that reference identifies an approved part, staff can verify the revision. If it does not, the enquiry stays open for clarification instead of becoming a guessed quotation.
For a Singapore supplier serving multiple customer sites, the delivery location or buyer name alone may not identify the equipment involved. Keep the request tied to the correct customer, project and source document. Do not reuse a previous customer’s mapping simply because the wording looks familiar.
Make the recommendation easy to inspect
A product specialist needs evidence, not a paragraph claiming the match looks good. Each candidate should show:
- The customer’s original wording and the source page or message.
- The proposed stock code, catalogue revision and relevant product-data reference.
- Which required attributes match, which conflict and which are missing.
- Whether it is an exact-code result, an approved alias or a suggested alternative.
- The unresolved question, reviewer and decision needed before quotation.
A search score can help order the list. It should not be presented as a percentage probability of technical compatibility unless that interpretation has actually been validated. Keep stock availability, lead time and authorised pricing as separate checks against their own current records.
Maintain the catalogue behind the matching
An outdated catalogue can produce a well-explained but unusable answer. Agree who maintains active and discontinued items, aliases, units, pack sizes, drawings and replacement references. Where supplier information conflicts with internal records, flag the conflict rather than silently overwriting an approved value.
Restrict the workflow to the sources it is permitted to use. Customer-specific pricing and private drawings should not leak into another account’s response. Refresh source data on an agreed schedule and retain the version used for each recommendation.
Test the difficult enquiries, not just exact matches
Build a staff-reviewed test set with complete product codes, abbreviations, obsolete items, incomplete specifications and requests that have no valid match. Include realistic variations from your own enquiries, with sensitive information removed where appropriate.
Measure whether the correct candidate is found, how often an unsuitable item is suggested, and how long reviewers take to reach a decision. A good test also checks whether the workflow stops when information is missing. The aim is less investigation per enquiry without hiding uncertainty.
Product matching is only one part of the process. Our guide to quotation workflow gaps covers the pricing ownership and approval steps that follow it.
How ADSM can support the workflow
ADSM’s RFQ and quotation automation service can assess a workflow from enquiry extraction through candidate matching and quotation preparation. The starting point is a representative set of enquiries, an approved catalogue and examples of the decisions your staff make today, including when they ask customers for more information.
For distributor and engineering workflows, matching rules, technical review and commercial approval should be agreed together. Connections to product records, CRM or ERP depend on available interfaces and are scoped separately through AI integration planning; a document library alone is not a product-matching engine.
Start with one product family and a small set of redacted RFQs. That gives the team a concrete way to agree what counts as a match, what requires clarification and what may proceed to a quotation draft.