Service

AI RAG Systems

Turn your docs, FAQs, and knowledge base into a 24/7 AI assistant.

I design and deploy production-grade Retrieval-Augmented Generation systems that let your team — or your customers — query any document, policy, or knowledge base in plain English. Built with automated ingestion pipelines that re-index content on update, semantic vector search for accurate retrieval, and OpenAI-powered conversational interfaces that stay in context.

60%

Support queries automated

85%

Bot accuracy on first response

70%

Ticket volume reduction (month 1)

What's included

  • Document ingestion pipeline with automatic re-indexing on update
  • Vector database setup (pgvector or Pinecone) with chunking strategy
  • Semantic similarity search tuned for your content type
  • Conversational query interface with memory and context window
  • Webhook or API endpoint for embedding into any product
  • Analytics layer to track query accuracy and escalation rate

How it works

01

Ingest & Embed

Your documents — PDFs, Notion pages, CSVs, website content — are ingested, chunked, and embedded into a vector database. The pipeline re-runs automatically whenever content changes.

02

Retrieve & Rank

When a user asks a question, semantic search retrieves the most relevant chunks from the vector DB. Context is assembled and ranked before being passed to the LLM.

03

Generate & Deliver

OpenAI synthesises a grounded, accurate answer from retrieved context. Unknown queries are escalated or flagged — the bot never hallucinates outside of your data.

Real projects using this service

Ready to get started?

Book a free 30-minute discovery call and let's scope out your project.

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