Search and retrieval
Semantic search over your documents, products, or knowledge base so users find what they need without exact keywords.
Traditional keyword search breaks when users don't know the right terms. AI-powered semantic search understands intent - so "how do I return a faulty item" finds your returns policy even if it never mentions "faulty".
I build AI search over documents, product catalogues, knowledge bases, and internal wikis. For manufacturing firms, that might mean finding specs or procedures by describing the problem. For retailers, it's AI product discovery that understands "something like X but cheaper". For professional services, it's surfacing the right precedent or template. The tech (embeddings, vector search) is proven; the work is making AI search useful for your data and your users.
Example AI integrations
AI services and tools I've integrated for businesses include:
Marqo
AI search API with built-in embedding and hybrid retrieval. For search, it powers semantic queries over documents and product catalogues.
Zilliz
Vector database for AI-powered semantic search at scale. For search, it stores embeddings and runs similarity queries at scale.
Pinecone
Vector DB for RAG and neural search over embeddings. For search, it indexes vectors for fast retrieval in RAG pipelines.
Weaviate
Vector database with hybrid search and built-in embeddings. For search, it combines vector and keyword search for hybrid retrieval.
Qdrant
Vector database for similarity search and filtering. For search, it enables filtered similarity search over embeddings.
LlamaIndex
AI data framework for RAG, retrieval, and semantic search. For search, it orchestrates indexing and retrieval for RAG applications.
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Types of businesses I work with
- Startups and scale-ups - Fast iteration on AI-powered products, from MVP to production-ready. Often working alongside founders or small engineering teams.
- Education and training - AI for learning platforms, assessment, content generation, and personalised learning paths.
- Healthcare and life sciences - Clinical documentation, medical records, research automation, and compliance for healthcare providers.
Frequently asked questions
- Keyword search only finds exact word matches. AI semantic search understands meaning and intent, so a query like "how do I return a faulty item" will find your returns policy even if it never uses the word "faulty". It works by converting text into numerical representations (embeddings) and finding the closest matches.
- Yes. I build search over whatever data you have - PDFs, spreadsheets, product catalogues, wikis, or internal docs. The AI indexes your content and makes it searchable by meaning, not just keywords.
- For queries where users don't know the exact terminology, AI search is significantly more accurate. It understands synonyms, context, and intent. For exact lookups like order numbers, traditional search still works well - most systems use a hybrid approach combining both.
- Yes. Even with a few hundred products, semantic search improves discovery by understanding what customers mean rather than requiring exact product names. The technology scales from small catalogues to millions of items.
What is the difference between keyword search and AI semantic search?
Can AI search work with our existing documents and product data?
How accurate is AI-powered search compared to traditional search?
Is AI search suitable for a small product catalogue?
Want to discuss AI for your business?
I help businesses integrate AI into their workflows. Get in touch to talk through your specific situation.