Product · Understanding context

It knows what you mean to ask,
and where the answer lives.

Before you ask it. The assistant already knows your patterns: who you are, what you work on, what happened lately and what comes next. So "What did we promise them?" lands the way it would with a colleague, and the answer comes back with proof.

One field of company context: the word Context at the core of four nested translucent rings, with Sales, Engineering, Marketing, Product, Support and Finance orbiting the rim, the sources they work across — Drive, Slack, Outlook, Jira, Salesforce, Teams — further out, and the kinds of record the answers come from inside the rings: contracts, invoices, policies, tickets, emails, meeting notes.

Naxis eliminates the longest part of asking: explaining what you mean.

Half of every question is setup — who "they" are, which deal, since when. The engine carries that setup for every person, across every connected source, so the short question lands and the answer arrives already holding its documents.

With context
0
retelling: who is asking, what they work on and what they asked before is already in the room.
15
minutes, at most, from a change in any source to the context carrying it.
1
click from any claim in the answer to the exact passage it stands on.
The index

Everything your company knows, one index.

Drives, mailboxes, chat, trackers, the ERP — every source feeds one permission-aware index, capturing the content, the people around it and who may read what, together.

Every data source
Source marks — Drive, Slack, Outlook, Notion, GitHub, Teams, Gmail and more — and two colleagues, all wired into one indigo index disc carrying the Naxis mark.
The language

It speaks your company's shorthand.

Ask with the words your team actually uses — initials, codenames, "their", "the latest". The entity registry resolves them to the right client, project and document before searching starts, so a four-word ask finds the governing contract.

How answers work
The ask 'Find their latest contract' resolving down three wires to the client, the latest version, and the contract itself in Drive.
The map

It doesn't just read your documents. It maps what they know.

Beyond indexing content, the engine distils it into a knowledge graph: people, companies, projects and deals, with the real relations between them. "Them", "that deal", "her team" — resolved to the right things before searching even starts.

See the graph in the live demo
A knowledge map: The client at centre, wired to People, Projects, Deals and Documents, with the colleagues, the governing contract and the systems that populate them.
Personal

Every answer arrives knowing who asked.

The same question means different things from finance and from support. Answers are situated in the asker's role, groups, history and permissions — and every claim lands with the citation that backs it.

How permissions work
One question — 'What did we promise them?' — branching to two situated answers: Support gets the response-time promise cited to Teams, Finance gets the settlement credit cited to the ERP.
Everywhere

The same context, wherever work happens.

Web chat, the channels your team already lives in, and an API & MCP plane for your own tools and agents — one context behind every door, permission-filtered at each.

Messaging & channels
A scattered field of channel doors — website chat at centre; around it Slack, Teams, WhatsApp, Telegram, Messenger, Viber, LINE, Gmail, Google Chat, Discord and Matrix — with two colleagues among them.
API & MCP

The same context, for your own tools and agents.

One admin key opens the whole deployment to your code and your AI assistants — ask, add documents, run the console. Plain REST on one side, MCP on the other, every action audited and permission-filtered.

See it in the live demo
Three overlapping doors — an ingestion tray, MCP, and the gradient-ringed deployment carrying the Naxis mark — wired to GitHub, GitLab, the web and your files.

Accurate AI answers, explained

How does it know what I mean without me spelling it out?

Because it already knows your patterns. It knows who you are, what you work on, what you asked before and what changed around you lately, so it reads a question the way a colleague at the next desk would, and it already knows which corner of the company record the answer lives in.

How can AI answer from company documents without hallucinating?

It is not allowed to speak without evidence. Every claim cites a passage from your own documents, and an answer it cannot back is an answer it does not give. You can stop worrying about invented facts; the mechanics are written up in the docs.

What happens when documents contradict each other?

It happens in every company, and the assistant is built for it: newer and more authoritative documents win, an executed contract outranks a draft, and the answer cites the document that carried the day so you can check it yourself.

What if the answer is not in our documents?

You get an honest "I don't have that in the knowledge base." No answer is invented; there is no answer without a source.

Does it handle dates and "the latest version" correctly?

Yes. Dates are computed, never guessed, and "latest", "current" and "as of March" resolve against the documents' own dates. If date handling is the thing your team worries about, the docs walk through exactly how it works.

Which languages does it understand?

Ask in your language and it answers in kind; Greek, English and mixed corpora are normal. Conversations persist per person, so follow-up questions carry their context.

Double productivity now

Live demo

Notes from the build.

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