AI Development
AI-Powered Search Development
Search that understands intent, not just exact keywords.
- Typical timeline
- 3-6 weeks depending on catalogue size and filter complexity
- Category
- AI Development
- Engagement
- Fixed-scope phases
- You receive
- Code, data & documentation
Overview
Keyword search fails the moment a user's words differ from yours. Semantic search matches meaning, so a search for "something for back pain" surfaces the right products and a search for "leave policy" finds the human resources handbook — even when those exact words never appear.
Your search box returns nothing, and users leave
Visitors type what they want in their own words. Keyword search demands your words, in your spelling, in your order. Every failed search is a visitor who concludes you do not stock what they need — on an e-commerce site, this is one of the most expensive problems to leave unfixed.
You will recognise this if
- Search frequently returns no results for products or documents you definitely have
- Users have to know your internal product naming to find anything
- A typo or a plural produces zero results
- You have no data on what people search for and fail to find
What you get out of it
The difference this makes
Found by meaning
Descriptive, conversational and misspelled queries all return relevant results, because matching is on intent rather than exact strings.
More of your catalogue gets seen
Items buried behind unusual internal naming become discoverable, which typically lifts the breadth of what actually sells.
Insight into demand
Search analytics show what visitors look for and what they fail to find — the most direct demand signal you can get.
Fast and filterable
Semantic ranking combines with ordinary filters for price, category, brand and availability, so power users are not slowed down.
What we build
- Hybrid search combining keyword matching with vector similarity
- Typo tolerance, synonym handling and plural forms
- Faceted filters alongside semantic ranking
- Instant as-you-type suggestions
- Merchandising controls: pin, boost or bury specific results
- Natural-language query support for descriptive searches
- Zero-result and low-engagement query reporting
- Incremental indexing so new items are searchable immediately
How it gets used
An industrial supplier with a large parts catalogue
Buyers describe a requirement in plain language or a partial specification and reach the right part without knowing the catalogue code.
A company intranet
Staff search policies, forms and procedures by what they need rather than by document title.
A fashion retailer
Shoppers search by occasion, fabric and style rather than SKU names, and merchandisers boost seasonal lines.
Technology we typically use
You do not need to care about this list — but your next developer will.
- pgvector / PostgreSQL full-text search
- Embedding models
- Typesense or Meilisearch
- Python with FastAPI
- Next.js
Timeline: Typically 3-6 weeks depending on catalogue size and filter complexity
Questions
AI-Powered Search Development — common questions
Something else on your mind? Ask us directly — it is usually faster than reading.
How is this different from the search we already have?
Will it slow the site down?
Can we control which results appear first?
Related services
Businesses that need this often need one of these as well.
Thinking about ai-powered search development?
Tell us about your situation. We will tell you whether this is the right service, what it would involve, and roughly what it would cost.