Semantic search development
We build search that understands what people mean, combining embeddings with keyword matching and your business rules. Customers find the right product and employees find the right document, even when their wording does not match yours.
What it is and when it fits.
Semantic search turns text, and sometimes images, into embeddings so that queries match on meaning. On its own that misses exact terms like part numbers and brand names, so we build hybrid search: vector and keyword retrieval combined, reranked and adjusted with filters, stock, margin or recency. We tune it on your real search logs and measure relevance with labelled query sets.
This is a strong fit for large catalogues with inconsistent product data, multilingual content, or knowledge bases where people search in their own words. If your users mostly search by exact code or your collection is small and well tagged, tuning your current keyword search may be enough. We check your search logs before recommending either.
What we build.
Product search
Catalogue search that handles synonyms, vague descriptions and typos, with ranking that respects stock, filters and merchandising rules.
Knowledge search
Search across documents, tickets and wikis that returns the relevant passage, filtered by the user's access rights.
Hybrid ranking pipelines
Vector and keyword retrieval fused and reranked, with scoring you can adjust without redeploying the model.
Multilingual search
Queries in one language matching content in another, useful for Dutch, English and German catalogues alike.
Relevance testing
Labelled query sets and offline metrics, plus click and conversion tracking to confirm changes improve real outcomes.
How it works.
- 01
Study the search logs
We look at what people search for, where they get zero or poor results and which queries matter most commercially.
- 02
Build a relevance baseline
We label a query set, measure your current search against it and set targets for the new ranking.
- 03
Build and tune
We choose embedding models, build the hybrid pipeline and tune ranking against the query set and your business rules.
- 04
Release and measure
New search runs alongside the old one on part of the traffic, and we compare conversion and engagement before switching fully.
Related work.
Built with.
All technologiesFurther reading.
Embeddings and semantic search: a practical guide for product teams
How embeddings power semantic search, how to choose a model and a vector store, and how to measure whether your results actually improved.
5 min read
How to evaluate RAG: retrieval metrics, faithfulness and golden sets
How to measure a RAG system properly: separate retrieval from answers, check faithfulness claim by claim and build a golden set you can trust.
4 min read
Common questions.
Often not. If you run Elasticsearch or Postgres, we can add vector search and reranking to it. We only suggest a separate vector database when volume, filtering or latency requirements call for one.
We test several on your data, including commercial models from OpenAI and open models from Hugging Face that can be self-hosted. The best model varies by language and domain, so the relevance set makes the decision.
Well-built hybrid search returns results fast enough for search-as-you-type on most catalogues. Reranking adds some latency, so we apply it selectively and cache where it makes sense.