RAG Development for Intelligent Knowledge Systems
Retrieval-augmented generation lets an AI answer from your documents instead of guessing. The quality depends almost entirely on ingestion, retrieval and evaluation — not the chat window.
The problem
Basic RAG demos split documents into chunks and hope the right one comes back. Answers are confidently wrong when it doesn't.
Permissions, stale documents and missing citations make many knowledge bots unusable inside a company.
The approach
We design ingestion around your document types, combine keyword and vector search, and return answers with sources.
Access control is enforced at retrieval time, and a test set of real questions measures quality before and after each change.
What we build
- Internal knowledge assistants
- Answer questions from policies, wikis and manuals with citations.
- Customer-facing help
- Support answers grounded in your product docs.
- Document search
- Semantic search over contracts, reports or tickets.
- Evaluation harness
- Repeatable tests that measure retrieval and answer quality.
Use cases
- Internal policy and HR assistant
- Product documentation support
- Sales enablement search
- Contract lookup
Typical architecture
Document sources
Ingestion + chunking
Embeddings
Vector + keyword index
Retriever with permissions
LLM answer with citations
Technology
- Python / FastAPI
- PostgreSQL + pgvector
- Embedding models
- LLM APIs or self-hosted models
Related work
Process
- 01
Understand
Understand the business problem and the outcome you need.
- 02
Architect
Choose the AI architecture, integrations and technical approach.
- 03
Prototype
Validate the critical workflow before building around it.
- 04
Build
Develop the product in milestones you can review.
- 05
Validate
Test quality, performance, reliability and edge cases.
- 06
Launch
Deploy the production system and monitor it.
- 07
Improve
Optimise using real usage and feedback.
Questions
RAG or fine-tuning?
RAG for knowledge that changes or needs citations; fine-tuning for style, format or narrow tasks. Many systems use both.
Can it run without sending data to a third party?
Yes, with self-hosted embedding and language models, at some cost to quality and speed.
Further reading
Tell us what you want to build.
Send a short brief. We'll reply personally with questions or a suggested next step — usually a technical discovery call.