In-App Semantic Search (Embeddings)

Replace the brittle keyword search inside your app or website with semantic search that understands intent. I generate embeddings (OpenAI text-embedding-3 or Voyage AI), store them in pgvector, Pinecone or Qdrant, and serve a fast search API your product calls directly. A shopper searching "comfy summer abaya" finds the right products even if those words aren't in the title — because the search ranks by meaning, not exact-match strings.

The build uses hybrid retrieval — BM25 keyword scoring fused with vector similarity — so you get the precision of keywords and the recall of semantics in one ranked result set. It's typo-tolerant, handles English and Arabic queries (including mixed-language and transliterated terms common in the UAE), and I tune relevance against your real catalogue or content so the top results are genuinely the best ones for a Dubai or Abu Dhabi audience.

Two important distinctions. Unlike my general AI Integration into Existing Apps gig — which adds any AI feature (chat, summarization, classification) — this is specifically in-product search. And unlike a RAG knowledge-base, which generates written answers to questions, this ranks and returns your actual records (products, listings, docs) by relevance. In short: it surfaces the right results, it does not write Q&A answers.

How it works

1. 1
   Index content

   I embed your products, listings or documents and load them into the vector store with a sync process.

2. 2
   Build search API

   I implement hybrid (keyword + vector) retrieval behind a fast API your app can call.

3. 3
   Tune relevance + ship

   I tune ranking on real queries, add typo and EN/AR handling, then ship it with documentation.