RAG & vector search development
AI answers that come from your documents, show which page they came from, and say “I don’t know” instead of guessing.
What we build
What we build with RAG & vectors
Knowledge assistants
Chat over your docs, wikis and tickets, citing the sources it used.
Search pipelines
Chunking, embeddings and hybrid search tuned to your content.
Permission-aware retrieval
People only get answers from documents they are allowed to see.
Why Widle
Answers you can trace back to a source
We use retrieval when answers have to come from your own documents and data, and we test that the answers stay grounded in them.
- 01
Quality you can measure
Retrieval and answer evals, so improvements are proven, not guessed.
- 02
Stays fresh
Automatic re-indexing when documents change.
- 03
Postgres-native
pgvector on Supabase or PostgreSQL; no extra database unless you need one.
Services
Services that use RAG & vectors
AI Agents & LLM Apps
Assistants that answer from your documents, use your tools and hand over to a person when they should, tested before they meet a customer.
Learn more →AI Full-Stack Development
AI products that hold up with real users: SaaS platforms, copilots and AI features inside your app, with the unglamorous parts (auth, billing, testing and monitoring) done properly.
Learn more →AI Wrapper Apps
Turn a prompt that works into a product people pay for: accounts, usage limits, billing and an admin panel on top of Claude, Gemini or OpenAI.
Learn more →Industries
Where we use RAG & vectors
RAG & vectors FAQs
Do we need a separate vector database?
Usually not. pgvector in PostgreSQL or Supabase handles most workloads.
How do you reduce made-up answers?
Grounding, citations, refusing when sources are missing, and evals on real questions.
Want answers from your own documents?
Tell us what you want it to do. In a free 30-minute call we’ll say whether RAG is the right fit and where we’d start.
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