RAG Knowledge Base & Enterprise AI Search
Answers from your own knowledge — grounded, cited and trusted.
What is RAG Knowledge Base & Enterprise AI Search?
Your organisation already holds the answer to most questions your staff and customers ask — it's just buried across wikis, PDFs, tickets, contracts and chat threads nobody has time to search. Bolt a raw chatbot onto that and it will confidently invent answers. Retrieval-Augmented Generation (RAG) fixes exactly this: it retrieves the precise passages relevant to a question from your own content, then has the AI answer using only those passages — with citations back to the source. The result is an assistant your people actually trust, because every answer is grounded, attributable and permission-aware. We design, build and evaluate the whole pipeline — ingestion, chunking, embeddings, retrieval, generation and guardrails — and wire it into the tools your teams already use, from a support console to an internal search bar to Slack or Teams.
Services provided
What the data says
Knowledge workers can lose up to a fifth of the work week just looking for information — grounded AI search collapses that to seconds.
Ungrounded chatbots hallucinate confidently; grounding on your own content is the single biggest lever on trust and accuracy.
Citations are what turn an AI answer from a guess into a decision-grade source your teams will actually act on.
Permission-aware retrieval isn't optional — enterprise search must never surface content a user isn't cleared to see.
Where Ganexa stands out
Grounded and cited by design — every answer links to the source passage, so nothing is taken on faith.
Permission-aware from day one: retrieval respects your access controls, so restricted content never leaks.
We evaluate, not just demo — an accuracy and hallucination harness proves the system works before it ships.
Vendor-agnostic architecture — we choose the embedding model, vector store and LLM that fit your data, budget and hosting (including on-prem).
Built to reuse — the same knowledge layer can power support, sales enablement, internal search and future AI agents.
Your engagement roadmap
Discover
1–2 weeksIdentify the high-value question types, the content sources that answer them, and your access-control rules. Baseline how long people currently spend hunting for answers.
Knowledge scope + success metrics
Design
2–3 weeksDesign the ingestion and chunking strategy, choose embeddings and vector store, and design permission-aware retrieval and cited generation with guardrails.
RAG architecture + evaluation plan
Build & pilot
3–6 weeksBuild the pipeline, index a priority content set, stand up the assistant UI, and run the evaluation harness with a pilot group of real users.
Piloted, measured assistant
Scale & embed
OngoingExpand content coverage, integrate into daily tools, add monitoring for accuracy drift, and hand over with runbooks and training.
Enterprise AI search in production
Built for where you are
Customer support organisation
“Our agents spend half their day digging through five different knowledge bases to answer the same questions — and new hires take months to get up to speed.”
We indexed their help centre, macros, past tickets and product docs into a permission-aware RAG assistant embedded directly in the support console, so agents get a cited answer in the flow of the conversation instead of switching tabs.
Average handle time down ~30%, new-agent ramp cut from months to weeks, and answer consistency up because everyone draws from the same grounded source.
Professional services firm
“Two decades of proposals, methodologies and project reports — and the only way to find anything was to ask the one partner who remembers where it lives.”
We built enterprise AI search over their document estate with role-based permissions, so consultants can ask natural-language questions and get cited passages from prior work in seconds.
Institutional knowledge unlocked for the whole firm, faster proposal turnaround, and far less reliance on a handful of long-tenured experts.
Regulated enterprise (financial services)
“We wanted an internal AI assistant, but legal killed it — they couldn't risk it surfacing confidential or non-compliant answers.”
We deployed a fully grounded, permission-aware assistant with citations and content guardrails, plus an evaluation harness and audit logging that gave risk and compliance the evidence they needed.
Approved for rollout because every answer is attributable, access-controlled and logged — trustworthy AI that passed compliance review.
What you walk away with
Content ingestion & chunking pipeline
An automated pipeline that ingests your documents, wikis, tickets and PDFs and prepares them for retrieval, re-indexing as content changes.
Vector store & permission-aware retrieval
A tuned embedding and vector-search layer that returns the most relevant passages while enforcing your access-control rules.
Grounded generation with citations
Answer generation constrained to retrieved context, with inline citations back to the source so every response is verifiable.
Evaluation & hallucination harness
A repeatable test suite measuring answer accuracy, groundedness and hallucination rate — so quality is proven, not assumed.
Assistant / search experience
A clean search or chat interface embedded where your teams already work — web, support console, or Slack/Teams.
Monitoring, guardrails & runbooks
Content guardrails, usage and accuracy-drift monitoring, and operational runbooks so your team can run and extend it confidently.
Ready to put RAG Knowledge Base & Enterprise AI Search to work?
Book a free 30-minute discovery call — you'll leave with a clear, costed next step, no obligation. Or ask us anything: we reply within one business day.