Service
Retrieval-Augmented Generation & Knowledge Systems
Retrieval-augmented generation (RAG) makes AI answer from your knowledge — policies, contracts, manuals, tickets, wikis — with citations. NexusNao builds retrieval systems where accuracy is engineered: chunking, embeddings, ranking, freshness and permissions all tuned to your corpus.
Problems we solve
Sound familiar?
Institutional knowledge trapped in PDFs, wikis, tickets and inboxes
Generic chatbots that can't answer company-specific questions
RAG prototypes that retrieve the wrong passages and answer confidently anyway
Sensitive documents leaking into answers for the wrong audience
Capabilities
What NexusNao delivers
Ingestion pipelines
Connectors, parsing, chunking and enrichment for documents, databases and SaaS tools.
Retrieval engineering
Hybrid search, re-ranking, metadata filtering and query rewriting tuned on your corpus.
Permission-aware answers
Access controls enforced at retrieval time so users only see what they're allowed to.
Citations & faithfulness
Every answer linked to sources, with eval suites measuring groundedness.
Freshness & sync
Incremental updates so answers reflect today's documents, not last quarter's.
Typical use cases
Where this lands first
- Internal knowledge assistants for support, sales and operations
- Policy and compliance Q&A with citations
- Contract and document analysis workspaces
- Customer-facing help centres that actually answer
- Research assistants over large document sets
Recommended technology
Tools we reach for
- pgvector
- Pinecone / Qdrant
- Elasticsearch
- Claude & GPT APIs
- Python
- Airbyte
Final technology choices are made per project, on evidence — never by default.
Delivery process
How the engagement runs
Corpus audit
Sources, formats, quality, permissions and update cadence.
Retrieval baseline
Build and measure retrieval quality on a golden question set.
Tune
Iterate chunking, embeddings, ranking and prompts to hit targets.
Operate
Sync pipelines, monitoring and relevance feedback loops.
Benefits
What you get out of it
Answers grounded in your documents, with citations
Search that understands meaning, not just keywords
Institutional knowledge that survives staff turnover
Permissions respected end-to-end
FAQ
RAG & Knowledge Systems questions
Straight answers to the questions teams usually bring us.
How accurate can RAG realistically be?
With a curated golden set, tuned retrieval and groundedness evals, well-built systems reach high, measurable accuracy on in-corpus questions — and are engineered to say 'I don't know' rather than guess when retrieval fails.
How is this different from fine-tuning?
RAG keeps knowledge in a retrievable store, so updates are instant, sources are citable and permissions are enforceable. Fine-tuning changes model behaviour, not knowledge freshness. Many systems combine both.
Can it handle our access permissions?
Yes — document-level ACLs are enforced at query time, so the same assistant gives different answers to different roles, matching your existing entitlements.
Related services
Often combined with
Intelligence, made operational.
Ready to talk rag & knowledge systems?
Share where you are today. We'll respond with an honest read on feasibility, timeline and the fastest route to value.