A private AI workspace for software teams, on your own server

Your codebase is your product, and pasting it into a public chatbot hands your unreleased work to a company you do not control. The function you just wrote, the architecture you are still arguing about, the feature you have not shipped: all of it leaves your machine and lands on servers in a US cloud, where you cannot see what happens next.

There is a calmer way to work. You can give your team a capable AI that runs on your own server, so the sensitive prompts about your own source code never leave your side. This post shows how kral does that, and what your team gets when the AI lives in-house.

Why a public chatbot is a bad fit for software teams

A public chatbot is built to ingest whatever you type. That is fine for a recipe. It is not fine for the diff that contains your core algorithm, the schema of your unreleased product, or the incident notes from last night. Once that text is in a vendor cloud, it is out of your hands, and you are trusting a third party with the one thing that makes your company worth more than its furniture.

The obvious response is to ban it. That does not work. Your engineers already paste code into a browser tab when they are stuck, and a policy memo will not stop someone at 11pm trying to fix a failing build. Banning AI just pushes it into the shadows, where you have no visibility at all. The honest move is to give people something good that you actually run, so the convenient option and the safe option are the same option.

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. When an engineer asks a question about your codebase, the prompt goes to your machine and stops there. No external API sits in the path, and nothing about your code crosses your network boundary.

Most teams do not go all or nothing. They wire up a strong cloud model for general work (writing a changelog, explaining an unfamiliar library, drafting an email) and keep a local model for the sensitive cases (your proprietary source, anything unreleased). Same workspace, same login, and your people choose the right tool without thinking about where the data goes, because you already decided that for them.

A full workspace, not a chat box

This is more than a place to type questions. Your team can build their own assistants in minutes, with no code. Set up an assistant that reviews and drafts code against your conventions, so reviews start from a consistent baseline instead of one person's taste. Set up another that turns a ticket into a clear spec, so the messy two-line issue becomes something an engineer can actually pick up.

You can save reusable routines so nobody rebuilds the same prompt setup twice. Drop in a document and ask questions about it. Pull a current answer from the web with citations when you need fresh information. And switch between the leading models in one click when one suits the task better than another. It is a workspace your developers will open every day, not a toy they try once.

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 can work with your own templates and internal knowledge instead of guessing from the open web. When it answers, it is drawing on what your team actually uses, not a generic approximation of it. The connection runs on your terms, and your systems stay yours.

You run it and you see everything

Because it lives on your infrastructure, 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. Wire it into single sign-on so access follows your existing accounts. It installs on Windows Server behind IIS, sits inside your network behind your firewall, and wears your own branding. For the wider picture of rolling this out across a company, see 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 across your team without the data leaving your side. Implementation consulting is part of what we offer, so the platform is working the way you want before your engineers ever log in.

Your code is the thing you are paid to protect. Give your team a capable AI that respects that, running where you can see it, on hardware you own.

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