1
The catalogue expected customers to know the product name
Technical buyers usually arrive with a requirement, not a perfectly formed catalogue query.
A conventional search works when the words typed resemble the product title. It is less helpful when a customer asks for a 2-inch brass ball valve with a lever, a WRAS-approved valve for drinking water, or the stainless equivalent of something they have already found. Those requests combine engineering attributes, synonyms and follow-up context in ways a keyword box does not naturally resolve.
The goal was not an open-ended support bot. It was a narrower product-finding layer that could understand the request, stay inside the published catalogue and hand the customer back something they could inspect or buy.
2
We made every product searchable as a set of engineering constraints
Each catalogue item became a compact passage of the attributes that actually distinguish it.
An offline indexing pass embeds product name, category, size, material, connection, pressure rating and WRAS status. The 1,126-product index is compressed to 1.5 MB and ships inside the existing Vercel function, where a full cosine scan takes less than a millisecond. At this catalogue size, a vector database would have added moving parts without improving the answer.
Cosine similarity finds the general meaning; a small lexical re-rank enforces explicit requirements such as brass, DN80, flanged or WRAS-approved. The best eight products go to a compact language model, which can explain the result but can only recommend from that retrieved set. Product cards are then attached from catalogue SKUs actually named in the reply.
3
The product finder went live without adding another platform
The widget runs on every page, remembers the conversation across navigation and drops customers onto a real product page.
In a small pre-launch staff test built around representative product-finding jobs, median time to a relevant product fell by 72%. All six retrieval benchmark shapes returned the right material, size and category within the top five, including follow-ups such as “what about in stainless?”.
The request path stays inside the existing Express-on-Vercel application. Inputs are capped and rate-limited, supplier and cost fields never enter the prompt, model output is escaped, and 29 API and browser checks cover realistic searches, context, prompt injection, data isolation and the complete reply-to-product-card flow. A typical message costs around a tenth of a penny.
Can customers describe the product before they can name it?
If your catalogue is technical, large or full of near-matches, a grounded product finder can shorten the route from requirement to product without replacing the systems already underneath it.