Visitors search and get "no results" even though the product exists
We search by meaning using embeddings (numeric fingerprints of a text's meaning). Synonyms, typos and descriptive queries still return results.
Site search that understands what your visitor means
Classic search matches words. Semantic search matches meaning: a query like "something warm for a mountain hike" finds jackets even if those words never appear in the description. We plug it into your site, catalog or knowledge base and measure whether more searches end with a useful result.
Quote and schedule after your brief
Who it's for
- Online stores with a large catalog where people search in their own words
- Sites with documentation, help centers or hundreds of articles
- Service companies whose clients don't know the exact names of what they need
- Products where support answers the same questions every day
Problems we solve
The right result is buried on page five
We combine meaning-based and keyword search, then add reranking. Exact SKUs and product names keep working.
We don't know what people search for and fail to find
We set up query logging. You see searches with no good answer and know which content or product to add.
We don't want to rebuild the site on a new engine
The search runs as a separate layer on top of your existing site. Your data and pages stay where they are.
What's included
- Data audit: what gets indexed, in what shape, how often it changes
- Text cleanup and splitting long pages into chunks
- Choice of embedding model and vector database (pgvector, Qdrant or similar) for your volume and budget
- Hybrid search: meaning plus keywords and SKUs
- Filters by category, price, availability or language
- Site integration: search bar, suggestions, results page
- Automatic index updates when you change the catalog or articles
- A test set of real queries to measure quality before and after
- Query log and a simple report on searches with no results
- Handover of access, code and documentation
Choose the scope
Use a package to describe what you need. We agree on the quote and schedule after reviewing your brief and requirements.
01
Pilot
Testing the idea on one part of the site
- One content type (catalog or articles)
- Basic hybrid search
- Test query set and before/after measurement
- Demo on a staging site
02
Production search
A store or a site with a knowledge base
- Full catalog or knowledge base indexed
- Filters and suggestions
- Integration into your existing site
- Automatic index updates
- Query log
03
Search with AI answers
Products with heavy documentation and support load
- Everything in Production search
- Short answers drawn from your content, with source links
- Multiple languages
- Rules for what the AI will not answer
How we work
Brief and requirements
You describe the task and current workflow. We clarify requirements, choose a first stage and prepare a quote.
Pilot with a measurable KPI
We agree on one metric, such as the share of searches that return a useful result, and record the baseline before launch.
Index build
We prepare the texts, compute embeddings, and set up hybrid search and filters.
Integration and testing
We connect it to your site and run the test queries, comparing against the baseline.
Launch and handover
We go live and hand over access, code and documentation. Payment is in stages.
Scope and limits
- We don't promise perfect results: rare queries will still miss, and we reduce those using the query log
- We don't guarantee higher sales: search removes a barrier, but price, stock and trust drive purchases
- We can't fix weak content: if catalog descriptions are empty or identical, those need improving first
FAQ
How is this different from the search I already have?
Standard search matches words in the text. Semantic search compares the meaning of the query with the meaning of your pages. It finds results through synonyms, descriptions or misspelled words.
What do you need from me?
Access to your catalog or text base (export, API or database) and examples of real customer queries. If you have no query log, we help set one up.
Does it work for languages other than English?
Yes. Modern embedding models cover many languages. We test quality on your own queries and on mixed-language content, such as foreign brand names.
Where does my data go?
That depends on the setup. We can use a cloud model or host everything on your own server. For sensitive data we agree on a no-third-party option before work starts.
What are the ongoing costs?
We prepare a quote after reviewing your brief. We list support and any third-party costs separately: subscriptions, APIs, hosting or media spend where the project needs them. Provider rates are checked before work starts.
How will I know search got better?
We build a test set of queries and measure quality before and after. We also track the share of searches with no results in the log. You see the numbers, not just us.
Start with your task
Describe your business, the outcome you need and your current workflow. We will clarify the requirements and prepare a quote after your brief.