RAG & Enterprise Knowledge Assistants
Retrieval-augmented assistants grounded in your own documents, so answers cite a source instead of inventing one.
Your company already has the answers. They are in a policy PDF, a Confluence page nobody has opened since 2023, a contract folder, and the head of operations' memory. Retrieval-augmented generation makes that material answerable in plain language — and, done properly, makes every answer point back at the document it came from.
Who this is for
Organisations where staff spend real time hunting for information that exists but cannot be found. Support teams answering from a knowledge base that is too large to know. Professional services firms with precedent buried in past work. Any company where one person's departure would take critical knowledge with it.
Problems we solve
- Retrieval that misses. The answer is in the corpus, the search does not surface it, and the model confidently improvises instead.
- Chunking that destroys meaning. Documents split mid-table or mid-clause, so retrieved passages are unusable.
- Stale answers. A policy updated three months ago that the index never learned about.
- Permission leakage. A general index that happily quotes the salary review to whoever asks.
- No citations. An answer nobody can verify is an answer nobody should act on.
What we build
- Ingestion pipelines for PDF, Word, HTML, spreadsheets, wikis, ticket histories and databases
- Structure-aware chunking that respects headings, tables and clause boundaries
- Hybrid retrieval — semantic plus keyword — because pure vector search fails on part numbers, codes and names
- Re-ranking to put the genuinely relevant passage first, not merely a topically similar one
- Permission-aware retrieval, so results respect who is asking
- Inline citations linking each claim to its source document and section
- Incremental re-indexing, so updated documents are reflected without a full rebuild
How we work
We build the evaluation set before the pipeline: a list of real questions with known correct answers and known source documents. That set is what tells us whether a chunking change or a re-ranker actually helped, rather than felt better. Retrieval quality is measured separately from generation quality, because most RAG failures are retrieval failures and blaming the model wastes weeks.
Technologies we use
Qdrant, pgvector and Pinecone for vector storage; OpenAI, Nomic and self-hosted Ollama embedding models; Claude, GPT, Gemini or local models for generation; LlamaIndex and LangChain for the pipeline; Redis for caching and rate limiting. Fully self-hosted stacks are available where documents cannot leave your infrastructure.
Business benefits
- Staff find authoritative answers in seconds rather than searching several systems
- Answers stay consistent regardless of who asks or who would have answered
- Knowledge survives staff turnover
- Citations make answers auditable, which matters in regulated work
- New starters become productive without a queue at a senior colleague's desk
Where it pays off
- Internal help desks for HR, IT and policy questions
- Customer support grounded in real product documentation
- Contract, tender and compliance document review
- Technical documentation search across large or fragmented product lines
- Onboarding assistants that answer from the actual handbook
Common questions
How much material do we need?
RAG is worth it from a few hundred pages upward. Below that, putting the documents directly into the model's context is simpler, cheaper and more accurate — and we will recommend that instead.
Can it respect our access permissions?
Yes. Permissions are enforced at retrieval, filtering by the asker's identity before anything reaches the model, so restricted material is never in the context to leak.
What about documents that change often?
Ingestion runs incrementally on a schedule or on a webhook from the source system, so only changed documents are re-embedded.
Will it admit when it does not know?
That is a design requirement, not a hope. When retrieval returns nothing above a relevance threshold, the system says so rather than generating from the model's general knowledge.
Sitting on documentation nobody can search? Point us at a sample and we will show you what is retrievable.