The people you support are often the least able to absorb a data leak, and a public chatbot is a leak waiting to happen with their case files. A name, an address, a diagnosis, a history of abuse, the moment any of it is pasted into a public chatbot it leaves your building and lands on a server you do not own, in a country you did not choose.
You do not have to choose between capable AI and protecting the people in your care. You can run a real AI workspace on your own server and keep the sensitive cases on your side. This is the way out, and it is simpler to put in place than you think.
Why a public cloud chatbot clashes with sensitive client data
Social services and nonprofits hold some of the most sensitive records anyone keeps: protection cases, health details, immigration status, financial hardship, family histories. A public chatbot is built to send every word you type to a vendor's cloud, usually in the US, where it may be stored, logged, or used to train the next model. For the people you serve, an exposure is not an inconvenience. It can mean real danger.
The instinct is to ban it. That does not work, because your staff already use it. A caseworker behind on notes will paste a referral into whatever is open in the browser, quietly, to save an hour. Banning AI just pushes it into the shadows where you cannot see it. The fix is not to take the tool away. It is to give people a safe one.
Run the model in-house
With kral the platform runs on your own server. You can add a local model on your own hardware, so a prompt about a named client goes to your machine and stops there. No external API sits in that path. The text never crosses your network edge.
Most teams run a mix. A cloud model handles the general work where nothing sensitive is involved (drafting a newsletter, summarizing a public report, rewriting a grant paragraph), and a local model handles the cases that name a real person. You decide which is which, and the sensitive prompts stay in the building either way.
A full workspace, not a chat box
Your team can build their own assistants in minutes, with no code. One worker can set up an assistant that drafts case notes in your format, so a few rough lines become a clean entry that matches how your service writes them. Another can build an assistant that turns a referral into a structured summary, pulling the key facts into the same shape every time. Once built, save reusable routines so nobody has to rebuild the same setup next week.
Beyond that, the workspace does the everyday things you would expect. Drop in a document and ask questions about it. Pull a current, cited answer from the web when you need a source. Switch between the leading models in one click when one suits the task better than another. It is one place your staff can actually work, not a single box that forgets everything.
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 templates and internal knowledge through a connector you control, instead of guessing from the open web. Your intake forms, your house style, your reference material: the assistant draws on what you point it at, and nothing more. Your systems stay yours.
You run it and you see everything
Because it lives on your side, you are in charge of all of it. Manage who is in and which models each person can use. Set a spending limit per person so costs never surprise you. Watch real usage on a dashboard, so the shadow AI problem turns into something you can actually see. Staff sign in with single sign-on. It installs on Windows Server behind IIS, sits inside your network behind your firewall, and wears your own branding. If you want a fuller picture of how this looks across an organization, read about company-wide AI you host yourself.
We help you put it in place
You do not have to do this 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 platform is working the way your service actually works before anyone relies on it.
Your duty of care does not stop at the screen. With a capable AI on your own server, you can give your staff the tool they already want and still keep the people you support out of someone else's cloud. That is the whole point.
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