Private AI Assistant
The assistant your team already uses, minus the data leaving.
Your engineers and analysts are already pasting company data into public chatbots. A private assistant gives them the same speed on your servers: grounded in your documents, aware of your permissions, with every conversation logged to your audit stack and nothing sent outside.
The problem is already inside the building
Blocking public AI tools does not stop usage; it moves it to personal phones. The workable answer is a sanctioned assistant that is genuinely good: fast, grounded in internal knowledge, and available where people work. Adoption kills shadow AI; policy alone never has.
NetRay deploys that assistant on your infrastructure. Under the hood it is a self-hosted open-weight model, a retrieval layer over your approved document stores, and a chat interface your team recognizes in the first five seconds.
Grounded in your knowledge, bounded by your permissions
A generic model knows the internet; your assistant should know your standard operating procedures, your part numbers, your contract clauses. We connect retrieval to your real sources (file shares, wikis, ERP extracts, PLM documents) with the permission model intact: a user only gets answers from documents they could already open.
Every answer cites its sources. That single feature is what turns skeptical senior staff into daily users, because they can check the work.
What the deployment includes
This is a product-grade rollout, not a container and good luck.
- Model serving sized to your concurrency, on your GPUs or a private cluster
- Retrieval over approved corpora with source citations and permission awareness
- SSO integration, role-based data scopes, and full conversation audit logs
- Admin console: usage analytics, prompt visibility, and kill switches
- Rollout support: champion onboarding and the free AI awareness workshop for leadership
Where this runs
Fully on-premise for regulated environments (the configuration we are best known for), in your private cloud tenancy when policy allows, or hybrid with classification-aware routing so restricted questions never leave the building. The data-grade decision is the first conversation we have, and our free simulator answers most of it in sixty seconds.
Straight answers
Is a private assistant as good as ChatGPT?
For general world knowledge, frontier hosted models still lead. For your work, a tuned open-weight model with retrieval over your documents is routinely better, because it knows your terminology, cites your sources, and does not hallucinate about your processes. Most internal workloads are your-work workloads.
How long does deployment take?
A working pilot with one document corpus typically lands in 2 to 4 weeks. Production rollout with SSO, permissions, and audit integration usually takes 6 to 10 weeks depending on how many source systems we connect.
Can it write and debug code too?
Yes. The same stack serves a self-hosted coding assistant with IDE integration. Many clients run both from one deployment; see our self-hosted coding assistant page for the specifics.
What happens to conversation data?
It stays in your database, under your retention policy, visible to your admins. That is the entire point: the assistant is inside your compliance boundary, so AI usage stops being an unmonitored exfiltration channel.
Give your team the assistant, keep the data.
We'll demo a private assistant grounded in sample documents from your world, then scope your rollout.
See it on your kind of dataFree AI workshops available. A founder reads every message.