AI Search Development
AI search development builds search that understands the meaning of a query, not just exact keyword matches — so users find the right document, product or answer even when their wording differs from the source text.
Benefits
- Fewer 'no results found' searches
- Finds relevant content even with different wording
- Works alongside existing search infrastructure
Problems It Solves
- Keyword search missing relevant results due to wording differences
- Users unable to find documents they know exist
- Product search that misses close matches
Who It's For
- E-commerce businesses
- Knowledge-heavy businesses
- Internal tools teams
Common Use Cases
- Semantic product search for an online store
- Internal document search across a large knowledge base
- Search-as-you-type suggestions grounded in meaning
How We Deliver It
- 1
Content indexing
We index your content using vector embeddings.
- 2
Search build
We build the search interface and ranking logic.
- 3
Tuning
We tune relevance against real query examples.
Technologies
- Vector embeddings
- Supabase pgvector
- OpenAI Embeddings API
FAQs
Tell us what you're trying to solve.
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