A private AI workspace for telehealth providers

Your whole service runs on patients trusting a screen with private symptoms, and a public chatbot in the workflow quietly breaks that trust. The moment a consultation note gets pasted into a free tool, those words leave your building and land on a server you do not own, in a cloud you cannot inspect.

There is a way to keep the help your team wants without handing the sensitive part to someone else. Run a capable AI on your own server, keep the consultation and the patient data on your side, and let staff work the way they already do. That is what this is about.

Why a public cloud chatbot clashes with telehealth and patient data

A public chatbot is built to take your text and process it somewhere else. For most office work that is fine. For a telehealth provider it is the wrong shape entirely. A symptom description, a named patient, a draft message about a diagnosis: each of those is exactly the kind of detail that should never leave your network. Once it is in a US cloud, you have no real way to know where it sits or who can reach it.

Telling staff to stop using AI does not solve it. They already use it. They paste a messy consult into a free tool on their phone because it saves twenty minutes, and they do it quietly. A ban just pushes the data further out of your sight. The fix is to give them something better that keeps the data in.

Run the model in-house

With kral the whole platform runs on your own server. On top of that you can add a local model on your own hardware, so a prompt about a named patient goes to your machine and stops there. No external API in the path, nothing leaving the building for that request. The text is processed where you can point at the box it lives in.

Most teams run a mix. A cloud model handles the general work where nothing sensitive is involved, drafting a policy, rewriting an internal memo, answering a question. The local model handles the sensitive cases, the actual consultations and the patient detail. You choose which is which, and the line is yours to draw.

A full workspace, not a chat box

Your team can build their own assistants in minutes with no code. One nurse might build an assistant that turns a recorded consult into a structured note, the same shape every time, ready to drop into the record. Someone else builds an assistant that drafts follow-up messages in your tone, so patients hear the same calm voice they got on the call. Save these as reusable routines and nobody has to rebuild the same setup next week.

Beyond that, the basics are there and they work: drop in a document and ask about it, pull a current answer from the web with the sources cited, and switch between the leading models in one click when one handles a task better than another.

Connect your own systems

kral supports MCP, the open standard for connecting tools and data to an AI. That means the assistant can work with your own intake templates and your internal knowledge through a connector you control, instead of guessing from the open web. It answers from what you actually use, not from whatever it scraped years ago. Your systems stay yours, and the connection runs on your terms.

You run it and you see everything

You manage who is in and which models each person can reach. You set a spending limit per person so nobody runs up a surprise. You watch real usage on a dashboard, so the picture is in front of you instead of buried in someone else's billing. Sign-on goes through your existing single sign-on. It installs on Windows Server behind IIS, sits inside your network behind your firewall, and wears your own branding so it feels like part of the practice. If you want the wider picture on this approach, here is more on running company-wide AI you host yourself.

We help you put it in place

You do not have to figure this out alone. We set kral up with you, connect it to your systems, and advise on rolling AI out to your team without the data leaving your side. Implementation consulting is part of what we offer, so the path from idea to working tool is one you walk with us, not one you reverse-engineer from a manual.

Patients trust you with the private part of their lives. Giving your team a capable AI does not have to put that trust at risk. Keep the model on your server, keep the consultations on your side, and let the work get faster without the cost of sending it away.

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