Retail

Semantic Search for Home Improvement: Project Queries and Compatibility

September 2026 · ISTRALLEN Team

People search for a job, not a part

Home-improvement shoppers search in terms of the task: "what do I need to tile a small bathroom," "quiet extractor fan for a kitchen," "connect a copper pipe to plastic." Keyword search returns whatever product names happen to contain those words. Semantic search for home improvement has to understand the project behind the query and return the parts that actually do it.

Home-improvement search classifies the query first. Part queries — “15mm copper to push-fit” — have their fitment attributes (size, material, standard, load rating) extracted and applied as exact filters, because a part that will not physically fit has failed even if it looked relevant; eligible parts are then ranked by task fit, DIY versus trade intent, and branch stock. Project queries — “tile a small bathroom” — are recognised as a multi-part job and routed to the component categories involved plus a project guide, rather than forcing one product to the top. Compatibility queries with weak results point straight at missing or inconsistent fitment data across suppliers, so the weekly query review becomes a concrete list of products to fix.

Unlike apparel, where a near match is fine, home-improvement parts have to fit each other — pipe diameters, thread types, voltage, fixing centres. A search that returns a plausible-looking fitting that will not connect to the shopper's existing plumbing has failed even though the result looked relevant. Compatibility data has to be retrievable and, where possible, filterable.

Composed product fields, not marketing copy

The embedding should be built from a field that carries the specification, the use, and the compatibility notes for each product, the same approach as our semantic search project. Raw marketing text describes benefits; a shopper matching a part to an existing installation needs the dimensions and standards.

Structured attributes stay as filters

Size, material, standard, and load rating work best as hard filters that narrow the set before the semantic match runs. "15mm compression, for copper" should reduce the candidates to things that genuinely qualify, then semantic ranking orders what is left by how well it fits the described job.

Project queries need a relevance floor

A broad query like "tile a bathroom" cannot be answered with a single product. The search should recognise a project-level query and either present the component categories involved or hand off to a project guide, rather than forcing a top result that is only one piece of the job.

DIY versus trade intent

The same query can come from a homeowner or a tradesperson, and they want different things — a starter kit versus a bulk box of a specific part. Signals like quantity language, professional terminology, and account type can tilt ranking without needing two separate search products.

Keeping stock and branch availability honest

Home-improvement purchases are often urgent and collected in person, so ranking has to account for stock at the shopper's chosen branch. A perfect match that is out of stock locally is a worse result than a good match on the shelf now.

A worked example

A shopper searches "connect a 15mm copper pipe to a plastic push-fit." The search reads the compatibility intent, filters to fittings that genuinely bridge copper and push-fit at that diameter, and ranks them by the qualities in the query and by branch stock. A second shopper searches "tile a small bathroom." The search recognises a project-level query, does not force a single product to the top, and instead presents the component categories — adhesive, grout, trim, spacers, sealant — with a link to a project guide.

What the query logs reveal

The searches that end in no click or a bounce are a map of the catalogue's data gaps. A run of compatibility queries with weak results usually means fitment attributes are missing or inconsistent across suppliers. Reviewing those queries each week turns search analytics into a concrete list of products that need better specification data.

Where this stops being right

Genuine compatibility certainty — will this specific part work with the shopper's exact installation — sometimes needs a person; the search should offer that path, not guarantee a fit.
Regulated work (gas, mains electrical) should point to qualified-tradesperson guidance rather than presenting parts as a DIY answer.
If your fitment and compatibility data is thin, semantic search will hedge; the first weeks of queries double as a data-quality audit.

FAQ

What does semantic search improve for a home-improvement site? It interprets task and project queries in plain language and returns parts that do the job, instead of matching product names to search words.

Why is compatibility such a focus? Because parts have to physically fit each other and the shopper's existing installation. A relevant-looking result that will not connect has still failed.

How should project-level queries be handled? By recognising them as multi-part jobs and presenting the component categories or a project guide, rather than forcing a single top result.

ISTRALLEN builds semantic search for home-improvement retailers with compatibility-aware ranking and honest branch availability — see AI for Retail.

See it in production

AI for Retail → Semantic search case study →

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