Most companies already own the knowledge they need. It sits in drives, mailboxes, chat threads, CRMs and databases, and the fastest way to find it is still to interrupt the one person who remembers where it lives. Naxis exists to remove that interruption without asking anyone to trust a platform.
This post states our goals plainly, so you can hold us to them.
One private knowledge engine per company
Naxis Assistant is a private knowledge engine: it answers questions from your company's own record, in your own deployment, and shows its evidence. Each client runs a complete instance with its own database. Documents, conversations, permissions and audit records never share storage with anyone else's. That is the architecture, not a pricing tier.
The goal behind that choice is accountability. A company must be able to answer for its data: where it lives, who read it, what left the boundary and why. Pooled platforms make those questions hard by construction. Single tenancy makes them answerable.
Answers that hold up
A knowledge engine is only useful if you can act on what it says. So Naxis is built around one rule: every claim cites a passage from your corpus, and citations are verified mechanically. When no citation survives, it answers "I don't have that in the knowledge base" instead of improvising.
Business corpora contradict and supersede themselves. Documents therefore carry their situation (date, author, the entities they concern, their standing as evidence), and an executed contract outranks a report, a report outranks notes, notes outrank drafts. Dates are computed deterministically; the model is not allowed to do calendar arithmetic.
We consider abstention a feature. A system that always answers is a liability wearing a chat window.
Nothing new to adopt
People do not switch tools because a vendor asked. Naxis answers inside the platforms a team already types in: Microsoft Teams, Slack, WhatsApp, the web console, or your own software through a plain API. Identity, permissions and citations follow each person through every doorway, and an unknown sender gets nothing until you decide otherwise.
Permissions inside the query
Two people can ask the same question and receive two different, correct answers, each scoped to what that person may read. The group filter lives inside every read the engine makes, not in any interface above it. There is no unfiltered path to the knowledge base, so there is no surface to misconfigure.
Paperwork that cannot drift
Compliance documents are usually written once and wrong within a quarter. A Naxis deployment generates its GDPR Article 30 record from the configuration that actually runs: retention values, sub-processors, technical measures. Erasure means deletion, store and search index alike. The audit log chains every entry to the one before it, so the record proves itself.
What we will not build
Naxis reads and answers. It does not act on other systems, score people, or make decisions about them, and deployments are contractually excluded from the uses the EU AI Act calls high risk. Client data is never used to train models: indexing runs entirely inside the deployment, and generation either stays fully in-house, on a self-hosted deployment that answers entirely on its own hardware, or uses the Naxis AI service under a data-processing agreement with zero-retention terms.
Where this is going
The product is live and the demo is a real deployment you can open today. The near-term work is steady: deeper source coverage. The security and compliance record is public and stays that way.
Open the live demo Security and compliance
If your company needs to answer for its data, that is the company we built this for.
