Turnyourscatteredinternaldocsintoonereliableknowledgeassistant
Aivora AI builds retrieval-augmented knowledge assistants that give employees accurate, sourced answers pulled from your wikis, policies, and files, so people stop hunting through five different tools for one answer.
0 minutes
Average time saved per employee search
Whythiskeepsstalling
Institutional knowledge is scattered across five tools
Policies live in one wiki, procedures in another, and the real answer is often in a Slack thread from eight months ago. Employees waste hours a week just locating information that already exists.
Search tools return documents, not answers
Traditional keyword search hands employees a list of possibly relevant pages and leaves them to read through each one. It doesn't synthesize an answer or tell them which document is actually current.
You're wary of AI hallucinating company policy
A confidently wrong answer about a compliance procedure or client contract term is worse than no answer at all. Without proper source grounding and citations, an internal AI tool can quietly create real risk.
Whattheengagementlookslike
Map your knowledge sources
We inventory your wikis, document stores, and file repositories to determine what should be indexed, how often it changes, and what access controls need to carry through to the assistant.
Build the retrieval and grounding pipeline
We build the indexing and retrieval layer so every answer is generated from your actual current documents, with citations back to the source, rather than from the model's general knowledge.
Test for accuracy and access control
We run the assistant against a set of real employee questions, checking both answer accuracy and that permission boundaries are respected, before anyone outside the project team can use it.
Deploy and keep the index current
We launch the assistant into your existing chat or intranet tools and set up automatic re-indexing so it stays current as your documents change, without manual upkeep from your team.
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Answer accuracy on evaluation set
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Documents indexed per typical deployment
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Weeks to production rollout
Beforeyoubookacall
Retrieval-augmented generation, or RAG, means the assistant first searches your actual documents for relevant content, then generates an answer grounded in what it found, with a citation back to the source. It is a meaningful step beyond a model simply answering from memory.