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.
Discuss your AI projectThe problem
01 — ContextBasic 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
02 — MethodWe 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
03 — CapabilityInternal 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
04 — Applications- Internal policy and HR assistant
- Product documentation support
- Sales enablement search
- Contract lookup
Typical architecture
05 — SystemProcess
06 — DeliveryUnderstand
Understand the business problem and the outcome you need.
Architect
Choose the AI architecture, integrations and technical approach.
Prototype
Validate the critical workflow before building around it.
Build
Develop the product in milestones you can review.
Validate
Test quality, performance, reliability and edge cases.
Launch
Deploy the production system and monitor it.
Improve
Optimise using real usage and feedback.
Questions
07 — FAQTell 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.