The AI Enterprise Search Assistant
Ask one question and get an answer drawn from Confluence, Jira, GitHub, and your vector stores — your internal knowledge assistant, grounded in private RAG and deployed on infrastructure you control.
Your answers exist — scattered across ten systems nobody searches well
The answer to most internal questions already exists in a wiki page, a ticket, a repo, or a document. Finding it means knowing which system to search and what it was called. New hires re-ask questions for months; experts get interrupted to answer them.
Knowledge is fragmented
Confluence, Jira, GitHub, shared drives, chat history — each has its own search, and none of them search each other.
Keyword search misses meaning
You have to guess the exact term the author used. Semantic intent — "how do we handle X" — falls through the cracks.
Experts are the bottleneck
When search fails, people ping a senior colleague. The org’s knowledge stays locked in a few overloaded heads.
Hosted AI can’t see your systems
A public chatbot knows the internet, not your internal wiki — and you can’t safely give it access to your private knowledge.
One assistant over all your knowledge
Unified
Search Every System From One Prompt
Confluence, Jira, GitHub, and your vector stores.
The assistant retrieves across your connected systems at once and synthesizes a single answer, rather than handing you ten tabs of keyword results. Native connectors mean it reads where your knowledge actually lives.
- Confluence, Jira, and GitHub connectors
- Vector search across document collections
- One synthesized answer, not ten tabs
- Follow-up questions keep context
One answer, every source
Trust
Grounded, Cited Answers
Every answer links back to where it came from.
Semantic retrieval finds the right passage even when the wording differs, and the assistant cites the page, ticket, or file behind each answer so people can verify and dive deeper. It flags uncertainty instead of inventing.
Links to the source
Control
Private, Role-Aware, On-Premise
People only see what they’re allowed to.
Retrieval respects access boundaries, so the assistant never surfaces content a user couldn’t open themselves. Deployed on-premise or in your sovereign cloud, your institutional knowledge powers the assistant without ever leaving your control.
On-prem · audited
Where an enterprise search assistant pays back
Employee Self-Service
Staff ask "what’s our policy on X?" or "how do I request Y?" and get a grounded answer instead of pinging a colleague.
Engineering Onboarding
New developers ask how a service works and get answers drawn from the repo, the wiki, and past tickets in one place.
Support Deflection
Agents and customers get instant, cited answers from your knowledge base, cutting escalations and resolution time.
Sales & RFP Lookup
Reps find the latest approved messaging, security answers, and product facts without waiting on enablement.
Tribal Knowledge Capture
Decisions buried in old tickets and threads become searchable, so the org stops re-learning what it already knew.
Cross-Team Discovery
Find out whether another team already solved a problem before you spend a sprint re-solving it.
What changes after rollout
Questions about the AI Enterprise Search Assistant
What is an AI enterprise search assistant?
It is an internal knowledge assistant that answers natural-language questions by retrieving across your connected systems — Confluence, Jira, GitHub, document stores, and vector databases — and synthesizing one cited answer. VDF’s assistant uses private RAG and runs on your own infrastructure, so it can safely search knowledge a public chatbot never could.
How is this different from Confluence or SharePoint search?
Built-in search is keyword-based and siloed to one system. The assistant searches semantically across all your systems at once, understands intent rather than exact terms, synthesizes a single answer, and cites its sources — while respecting each user’s access permissions.
Does it respect our access controls?
Yes. Retrieval is role-aware, so the assistant only surfaces content a given user is permitted to see, and every query is logged. Combined with on-premise deployment, that meets the bar for sensitive internal knowledge.
Will it hallucinate answers?
The assistant is grounded in retrieved passages and cites them, and is instructed to say when it cannot find a confident answer rather than invent one. Reviewers can always click through to the source.
Where does it run?
On-premise or in your sovereign cloud. Your knowledge base powers the assistant without any content being sent to a third-party model provider.
Agents that work well alongside this one
Related resources
Give every employee an answer engine for your own knowledge
See the AI Enterprise Search Assistant answer across your wikis, tickets, and repos — privately.