Enterprise Search & RAG
Your answers exist. Nobody can find them.
Knowledge is scattered across SharePoint, Slack, Confluence, drives, ticketing systems, and inboxes. Employees spend hours a week hunting for documents that already exist — or worse, redoing work because they never found them.
What is enterprise search?
Enterprise search is a permission-aware search layer that connects a company's document stores, wikis, chat, tickets, and databases into one place to ask questions. Mufasa Labs builds enterprise search using retrieval-augmented generation (RAG): AI answers grounded in your own content, every answer citing its source, and users only ever seeing results they already have access to.
Who it helps
Built for the people carrying the load
IT & knowledge teams
Stop being the human search engine for the rest of the company.
Operations & support
Find the current policy, contract, or runbook in seconds — not the 2022 version.
Every employee
One place to ask a question and get a sourced, permission-aware answer.
In practice
One search bar for everything your company knows.
Knowledge is scattered across SharePoint, Slack, Confluence, drives, ticketing systems, and inboxes. Employees spend hours a week hunting for documents that already exist — or worse, redoing work because they never found them.
What we build
Everything the system needs to hold up in production
Unified search layer
Connectors into your document stores, wikis, chat, tickets, and databases — indexed with your permissions intact.
AI answers with citations
Natural-language Q&A grounded in your content. Every answer links to its source so people can verify.
Permission-aware by design
Users only ever see results they already have access to. Access rules sync from your identity provider.
Search analytics
See what people ask and can't find — a live map of your knowledge gaps.
How it works
From kickoff to measured outcome
- 01
Connect
We inventory your systems and stand up secure connectors to the highest-value sources first.
- 02
Index & secure
Content is indexed with document-level permissions mirrored from your identity provider.
- 03
Pilot with one team
A single department uses it for two weeks. We measure answer quality and time saved.
- 04
Roll out & tune
Company-wide launch with adoption tracking, relevance tuning, and quarterly reviews.
Outcomes
What good looks like
Why teams trust us
No leap-of-faith moments
- We've built search over messy, decades-old content estates during large migrations — we know what real enterprise data looks like.
- Permissions are enforced at the index level, not filtered after the fact.
- You own the deployment: your cloud, your data, your keys.
FAQ
Enterprise Search & RAG: common questions
What is retrieval-augmented generation (RAG)?
RAG is the architecture behind trustworthy AI answers: instead of the model answering from memory, it first retrieves the relevant passages from your indexed content, then generates an answer grounded in — and cited to — those sources. It's how enterprise search avoids hallucinated answers and keeps responses current with your documents.
How is AI enterprise search different from SharePoint or built-in search?
Built-in search only covers its own silo and returns links, not answers. An AI enterprise search layer indexes every system — SharePoint, Slack, Confluence, drives, tickets, databases — and answers natural-language questions with citations back to the source document, so people can verify before they act.
How does enterprise search handle permissions and security?
Permissions are enforced at the index level, mirrored from your identity provider, and kept in sync. A user can only ever retrieve results they already have access to in the source system — there is no after-the-fact filtering that can leak content. The deployment runs in your cloud with your keys.
Which systems can enterprise search connect to?
Document stores like SharePoint and shared drives, wikis like Confluence, chat platforms like Slack and Teams, ticketing systems, email, and databases. We inventory your systems during discovery and stand up secure connectors to the highest-value sources first.
How long does an enterprise search implementation take?
A working pilot on your own content typically takes about two weeks. One department uses it for two weeks while we measure answer quality and time saved, then we roll out company-wide with relevance tuning and adoption tracking.
Is our company data used to train AI models?
No. Answers are grounded in your indexed content at question time — your documents are retrieved, cited, and never used to train shared models. You own the deployment: your cloud, your data, your keys.
How to start
Book a 30-minute scoping call. You'll leave with an honest read on feasibility, a rough timeline, and a fixed-scope path to a pilot — whether or not you hire us.