A Private AI Chatbot Trained on Your Stuff
A public chatbot is convenient until you realize every question your staff asks it — every contract, price and customer detail pasted in — is a copy on someone else's servers. We build a private AI chatbot trained on your own documents and host it on a server you own. Same instant answers, none of the exposure.
You want an assistant that knows your business
You want an assistant that knows your policies, your product specs, your past tickets. Public chatbots don't know any of that, and feeding it to them means leaking it.
The "enterprise" tier that promises privacy just charges more per seat for the same cloud. A chatbot trained on your documents — and kept inside your walls — gives you the knowledge without the exposure.
Trained on your documents
We index your manuals, policies and records into a private retrieval layer so the bot answers from your knowledge, with citations.
Self-hosted, no data out
Runs on your server, or one we build. Prompts and answers never touch a third-party API.
Staff + customer modes
An internal assistant for your team and, optionally, a customer-facing bot on your site — both on the same private model.
Built in plain Python
A standard, documented stack so you own it and can extend it, with a runbook and a Texas team to call.
Public cloud chatbot vs. a private one you own
| Public cloud chatbot | TIS private chatbot | |
|---|---|---|
| Knows your business | No (or you paste data in) | Yes, trained on your docs |
| Where prompts go | Vendor cloud | Your server |
| Cost | Per seat / per message | One-time build + optional support |
| Customization | Generic | Tuned to your content |
| Offline on your LAN | No | Yes |
Want it to act, not just answer? See AI agents for business. Learn how we keep it private and host it yourself.
A chatbot that keeps its answers inside your building
For businesses in Fulshear, Simonton and Wallis where customer and contract data can't go wandering, we build the bot, train it on your files and leave it running on your own LAN — every prompt and answer stays in the building. See our Texas service areas.
Private chatbot questions
What does "private" mean for a chatbot exactly?+
The model and your documents run on your hardware; no prompt or answer is sent to an outside service.
Can it answer from our own documents?+
Yes. We index your files into a private retrieval layer so it answers from your content, with sources.
Do we pay per message or per user?+
No. It runs on open models on your server, so there is no per-message or per-seat meter.
Can it go on our website for customers?+
Yes, optionally — a customer-facing version on the same private model, scoped to what you want it to share.
What model powers it?+
An open model such as Llama or Mistral, sized to your hardware, so you are not tied to one vendor.
Can it pull from our Google Drive, Confluence, or file share?+
Yes. We connect to the document sources your team already uses — shared drives, a file server, exported wikis such as Confluence, or a synced copy of Google Drive — and index that content into the private retrieval layer. The files are indexed locally on your server; the connection pulls your documents in, it does not push them out.
Does anything we type get sent to OpenAI?+
No. The chatbot runs an open model on your own hardware, so every prompt, document and answer stays on your server. Nothing is sent to OpenAI, Anthropic, or Google. That is the whole point of a private chatbot versus a public one.
Back to Business Automation · the main-site overview · the automation FAQ.
How it knows your business — RAG in one paragraph
The chatbot does not memorize your documents. Instead, when someone asks a question, it retrieves the most relevant passages from your indexed files and answers from those — a technique called retrieval-augmented generation, or RAG. That is why it can cite its sources and why a changed document is current the moment you re-index it, no retraining required. It is the difference between an assistant that knows the internet and one that knows your business.
If you want the full plain-English walkthrough of how that retrieval works — chunking, embeddings, citations and all — read RAG for business, the flagship explainer this chatbot is built on.
Chat front ends we deploy
The interface your team actually talks to is a self-hosted chat front end. Two mature, open options cover most needs — we pick based on how you work, not on a favorite.
| Feature | Open WebUI | AnythingLLM |
|---|---|---|
| Citations | Built-in RAG with source citations | Citations with hybrid search and reranking |
| Multi-workspace | Shared model with per-user chats | Separate document workspaces by team or project |
| Multi-user / SSO | Multi-user with role controls and SSO options | Multi-user workspaces with access controls |
| Best for | A polished, general team chat front end | Organizing many distinct document sets cleanly |
Both run entirely on your hardware, so the chat interface, the model, and your documents all stay in the building.
Citations: trace every answer to its source
An answer your team cannot verify is an answer they will quietly stop trusting. Because the chatbot writes from retrieved passages rather than memory, we can attach each answer to the exact document and section it came from. A staff member clicks the citation, reads the source, and decides — they are never asked to take the AI's word for it.
This is also the honest guardrail against made-up answers. A cited response either points to a real passage in your files or, when nothing relevant was found, says it does not know. We tune the retrieval so that trail stays clean and every answer is traceable back to where it came from.
Give your team an assistant that knows the business
We'll train a private chatbot on your documents and host it on a server you own — set up on-site in the Houston area. No per-message pitch.