A confidential AI workspace for biotech and pharma R&D

Years of research can be undone by one prompt, when an unpublished result is pasted into a public chatbot and lands on someone else servers. A sequence, a screening hit, a paragraph from a draft paper, once it leaves your network you cannot recall it, and you no longer know who reads it or what it trains.

There is a calmer way to work. Your team keeps the speed of a capable AI and keeps the unpublished science on your side, on hardware you run, where a sensitive prompt never crosses into a cloud you do not control.

Why a public cloud chatbot clashes with R&D that has not published yet

Biotech and pharma R&D lives on results that are not out yet. Target ideas, assay data, candidate structures, the methods section of a paper still in review. A public chatbot in a US cloud is the wrong place for any of that, because the moment it is typed in, the work sits on infrastructure your legal and IP teams cannot inspect. Provenance matters for patents, and you cannot prove a result stayed confidential once it has been pasted into a service you do not own.

Banning AI does not solve it. Your scientists already use these tools at home and on personal accounts, and a ban just pushes that use out of sight where you have no record and no control. The honest fix is to give them something good enough that they stop reaching for the public option.

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 You can add a local model on your own hardware, so a prompt about an unpublished result goes to your machine and stops there, with no external API anywhere in the path. The data never leaves the building. Most teams mix the two: a cloud model (Claude, GPT, Gemini) for general drafting and literature work, and a local model for the sensitive cases where the content must not leave your network. Each person picks per task, and the routing is yours to set.

A full workspace, not a chat box

Your team can build their own assistants in minutes, with no code. One scientist sets up an assistant that drafts internal research summaries from raw findings, so the weekly write-up takes minutes instead of an afternoon. Another builds an assistant that turns messy lab notes into a structured record, ready to file. Useful setups get saved as reusable routines, so nobody rebuilds the same configuration twice and good prompts spread across the group. Drop in a protocol or a PDF and ask questions about it directly. Pull a current, cited answer from the web when you need outside context. Switch between the leading models in one click when a different one fits the job better.

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. Answers come grounded in your material, in your house style, with your terminology. Your systems stay yours, and the connection runs on your terms.

You run it and you see everything

You decide who is in and which models each person can use. Set a spending limit per person so costs never drift. Watch real usage on a dashboard, by user and by model. Sign-in goes through your single sign-on. It installs on Windows Server behind IIS, sits inside your network behind your firewall, and carries your own branding so it feels like an internal tool, because it is one. If you want the wider picture on running this across a whole organization, read about company-wide AI you host yourself.

We help you put it in place

You do not have to stand this up alone. We set kral up with your team, connect it to your systems, and advise on rolling AI out across the group without the data leaving your side. Implementation consulting is part of what we offer, so the move from a scattered, unmanaged habit to a controlled in-house setup is something we do together.

Keep the speed your scientists want, and keep the unpublished work where it belongs: on your server, under your control, out of any cloud you cannot see into.

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