# Typesense Product Search

**A search box that understood a half-inch brass faucet.**

The catalog was full of part numbers, fractional dimensions and trade abbreviations. Default platform search returned nothing for most of the queries customers actually typed.

Canonical: https://technologiesninja.com/project-typesense-search.html

- Category: Search · eCommerce
- Result: 12ms p95
- Engine: Typesense · self-hosted
- Status: In production

## The problem — Zero results on real queries.

Customers searched the way they speak: half-inch rather than 0.5", part numbers without dashes, brand names spelled phonetically. Default search matched literal strings and returned nothing.

Because nothing was logged, nobody knew how often it happened. The failure was invisible in every report while being obvious to every customer.

Where results did come back, ordering was driven by text-match score rather than anything commercially sensible, so in-stock, high-margin products sat below discontinued lines.

## The hard parts — What made relevance hard.

- The same dimension appeared in four notations across the catalog and had to be normalised
- Part numbers needed to match with or without separators, and partially
- Trade synonyms and phonetic spellings existed only in the heads of the sales team
- Ranking had to weigh stock and commercial priority alongside text relevance
- The index had to stay in step with a catalog that changed daily

## Architecture — Index to result set.

01. **model** — Catalog fields mapped to searchable, filterable and ranking attributes
02. **normalise** — Dimensions, units and part-number formats standardised on the way in
03. **index** — Products written into self-hosted Typesense, updated on catalog change
04. **enrich** — Synonyms and abbreviations added from real query logs and sales input
05. **query** — Search hits Typesense directly with facets and typo tolerance applied
06. **rank** — Ranking rules weigh stock and commercial priority with text score
07. **learn** — Queries and zero-result terms logged and fed back into synonyms

**Stack:** Typesense · Laravel · Shopify · MongoDB · Redis

## What shipped — What shipped.

- Faceted search across products and collections with structured filter groups
- Typo tolerance and unit normalisation for fractional dimensions
- Partial and separator-insensitive part-number matching
- Synonym sets maintained from real search logs
- Ranking rules that account for stock and commercial priority
- Search analytics: top queries, zero-result terms, click-through by position

## Result — Fast, but relevance was the win.

**12ms** — p95 query latency · p99 under 15ms

Ninety-five per cent of queries return in twelve milliseconds, with p99 under fifteen. Speed was the easy part.

The change that mattered was relevance: the queries that previously returned nothing now return the right product, and zero-result terms are logged so the synonym set keeps improving.

Search analytics also became a demand signal — the team can see what customers ask for and do not stock.

## Read next

- Case study: [Shopify Attribution Tracking](/project-shopify-attribution)
- Case study: [AI Customer Support System](/project-ai-customer-support)
- Service: [Search & Discovery](/service-search-discovery)
- Article: [Typesense vs Traditional Search for eCommerce](/blog-typesense-vs-traditional-search)
- Case studies: [All case studies](/projects)

## Next step

What do your customers search for and not find?

- Book a consultation → /contact

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