A private AI workspace for schools

A child school record is exactly the kind of data parents expect to stay in the building, not on a chatbot vendor servers. One name, one note about a family situation, one line about a diagnosis, and the moment a teacher pastes it into a public chatbot, that text leaves your control and lands on a US cloud you do not run.

You do not have to choose between giving staff a capable AI and keeping student records on your side. There is a way to do both, and it runs on hardware you already own.

Why a public cloud chatbot is the wrong place for student records

A public chatbot is a stranger that remembers. Every prompt travels to a vendor, gets stored on their terms, and may be read by their systems. For an office full of general questions, that is fine. For a child protection note or a parent email about a custody case, it is a quiet leak that nobody signed off on.

Banning AI does not fix this. Your staff already use it, on their phones, at home, on the school laptop when nobody is watching, because it saves them an hour on the report comments. A ban just pushes that use into the shadows where you cannot see it. The honest move is to give them something better, inside walls you control.

Run the model in-house, so the sensitive prompt never leaves

With kral the whole platform runs on your own server. You can also add a local model on your own hardware, so a prompt about a named student goes to your machine and stops there. No external API sits in that path. The text about that child does not travel anywhere.

Most schools run a mix. A strong cloud model handles the general work, drafting a newsletter, summarizing a long policy, answering a curriculum question, because none of that is sensitive. The local model handles the cases where a real student is named. You decide 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 teacher sets up an assistant that drafts parent communications in your school tone, so the message about a missed deadline reads warm instead of curt. Another builds an assistant that turns a page of rough notes into a clean report comment, ready to drop into the system. Once built, those routines are saved and reusable, so nobody rebuilds the same setup next term.

It does more than write. Drop in a document and ask questions about it. Pull a current, cited answer from the web when you need a real source. Switch between the leading models in one click when one handles a task better than another. It is one place where the work actually gets done.

Connect your own systems

kral supports MCP, the open standard for connecting tools and data to an AI. Through a connector you control, the assistant works with your own report templates and your internal knowledge, instead of guessing from the open web. Ask it to follow your reporting format and it follows yours, not a generic one it invented. Your systems stay yours, and you choose exactly what the assistant can reach.

You run it, and you see everything

You manage who is in and which models each person uses. Set a spending limit per person so costs never surprise you. Watch real usage on a dashboard, sign in with single sign-on, and keep it all in your own branding. It installs on Windows Server behind IIS and sits inside your network, behind your firewall, where the rest of your data already lives. If you want the wider picture of running this kind of tool yourself, read about company-wide AI you host yourself.

We help you put it in place

You do not set this up alone. We install kral with you, connect it to your systems, and advise on rolling AI out across staff without the data leaving your side. Implementation consulting is part of what we offer, so the first local model and the first connector are working before your team ever logs in.

Give your staff a capable AI they will actually use, and keep the records that matter on your own server. That is the whole idea, and you can have it running on hardware you already own.

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