Connected, synced sources
We connect Google Drive, SharePoint, Notion, Confluence or your help center. Each passage is stored with its title, date and link, and the sync picks up documents that change or get deleted.
Nexura Digital service
An AI knowledge base (RAG) that answers from your documents and cites them
Your team asks in plain language and the assistant answers with what your documents actually say, linking the exact source. When the answer isn't there, it's set up to say so.
An AI knowledge base built on RAG (retrieval-augmented generation) finds the relevant passages in your company documents and writes the answer from them, citing the source. We deploy it for teams that lose time hunting for internal information or repeating the same answers. You start with a 7-day trial on a single source, using your team's real questions.
What's included: sources, permissions and citations
Permissions that hold
Each passage carries team or access-level metadata, and search filters by who is asking. Anything that shouldn't be searchable stays out of the index, and we agree with you on how to reflect very granular permissions.
Answers with citations
Every answer links the document and section it comes from, so anyone can check it, and the assistant is instructed to say it can't find something rather than improvise. RAG reduces made-up answers but doesn't eliminate them.
Who it's for, and who it isn't
A good fit if
Your team checks procedures, spec sheets, policies or support history every day, and that documentation exists and is reasonably current.
Not a fit if
- You only have a handful of documents: a good FAQ page will do
- Your documents contradict each other and nobody decides which is right
- You need exact figures from a live database: an integration or an MCP server fits better
- A mistake would have serious consequences and there will be no human review
How we measure answer quality
An unevaluated assistant is just a demo, so we test it against real questions before the team uses it.
What we check
- Whether search retrieves the right document
- Whether the answer sticks to what was retrieved
- Whether the citation points to the right place
- Whether it admits not knowing when the information isn't there
How we check it
Your team writes questions with the expected answer and its source. We review by hand and with a second model as a judge, and rerun the set after every change to the model, chunking or instructions. Once live, user votes and unanswered questions reveal documentation gaps.
How we roll it out
Preparation
- Choose sources, owners and what stays out
- Remove duplicate or outdated versions
- Define access groups and the channel to use
Build
- Ingestion and sync with n8n or code
- Vector index with hybrid search, in Qdrant or in Postgres with pgvector
- Assistant in Slack, Microsoft Teams or an internal chat
- Evaluation, tuning and a gradual rollout
What stays in place
Deliverables
- Ingestion and sync workflows on your server
- Vector index in an account in your name
- Assistant set up in the channel you chose
- Map of sources and permissions
- Evaluation set with its results
First step: a 7-day trial with one source
A scoped, measured assistant
We choose one source with you, such as one department's procedures folder, and one channel. We build the assistant with citations, run your team's questions and review the results together before adding sources. Scope, dates and terms are agreed in writing before we start.
FAQ
Frequently asked questions
Will my documents be used to train the model?
With RAG, the model isn't retrained on your documents; it only receives the passages needed for each answer. We choose providers whose API terms exclude that data from training and, if data can't leave your infrastructure, we look at open models on your own server. The legal assessment is up to your advisor.
Can it respect the permissions we already have in Drive or SharePoint?
Yes, with caveats. We filter by groups or collections based on who is asking, as long as the channel, such as Slack or Teams, identifies the person. Mirroring very granular folder permissions exactly is more complex, so we agree on a clear access model before indexing.
Does it work with scanned PDFs, tables or Excel files?
Scanned PDFs need OCR, and complex tables lose their structure if they're split badly, so we handle them separately. Spreadsheets full of figures are better queried through a direct integration, because the assistant shouldn't calculate from fragments. During the assessment we flag the documents that will cause trouble.
Who maintains the knowledge base afterward?
Your team owns the content: when a procedure changes, the assistant reflects it after the next sync. We leave ingestion automated, alerts if a sync fails and a guide for adding sources. If you'd like us to keep reviewing evaluations and tuning, that's agreed separately.
Contact
What question does your team ask most often?
Send us an example and tell us where the answer lives today. We'll propose a 7-day trial on that source, with results to review before you decide.
Tools
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Tools
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