Search that matches intent, not only the words typed. Embeddings and a vector index sit in front of a large catalog so a query can find the right item when the wording differs.
Exact keywords miss items when the catalog and the query use different phrasing. Shoppers and staff then page through near-misses.
This build embeds the catalog and the query in the same vector space, retrieves nearest neighbors, and returns a ranked list. It is the catalog matcher. The legal-document case study and the resume matcher are separate projects.
Each catalog record is stored as a vector so similar meaning sits near similar meaning.
A query is embedded the same way and compared with the index. Results are nearest neighbors, not a LIKE scan.
The list can be re-ranked with business rules after the semantic pass, so relevance and catalog constraints both apply.
Large product and content catalogs where the words in the query are not the words on the record.
Semantic search case study is the pet-adoption deployment. Semantic Job Matcher is the resume-to-role tool.
No. Resume-to-role matching is the Semantic Job Matcher. This page is catalog search: a query in, ranked items out.
The semantic pass does not require the query to contain the record’s exact words. A later ranking step can still apply filters the catalog already has.
On the semantic search case study, linked from case studies.
Empowering AI-Driven Automation & Transformation. Tell us whether you need a custom web build, an AI model, or a product like Voice AI Copilot.
3672 S 16th Ave, Arizona, 85713
info@atwebtechnologies.com
(480) 743-4017
