Professional
The assistant for one person's working day: research, reading, drafting.
- Chat with persistent context
- Document upload and analysis
- Drafting in multiple languages
- Export to your own tools
A European artificial intelligence company
An AI assistant for people who have to account for the answer: chat, document analysis and drafting, built to enterprise expectations.
Most assistants are built for a question and a paragraph back. Professional work is not shaped like that. It is a folder of documents nobody has time to read end to end, a draft that has to survive review, a thread that runs for three days and picks up new material halfway through, and — somewhere at the end of it — a person who has to stand behind what was written.
Aleph Alpha is an AI assistant built around that reality. You hold a conversation with it, but you also hand it material: contracts, reports, specifications, correspondence, research. It reads them, answers against them, and tells you which part of which document it is answering from. When you ask it to draft, it drafts in your language and in the register your colleagues expect, not in the flat cheerfulness of a chatbot.
It is a European product, and that is a technical statement rather than a flag. The architecture, the hosting and the data handling are designed for organisations that have to explain, in writing, where their information went and what processed it. For a lot of our users that question arrives before any question about model quality — so we answer it first.
Ask follow-ups without restating the background. The assistant holds the material and the decisions already made in the conversation, so the fourth question is as informed as the first.
Upload a set of documents and ask across them: what changed between versions, which clause covers this case, what the report concludes on page forty. Answers point back to the passage they came from.
Summaries, briefing notes, customer replies, internal memos, first drafts of long documents — generated from your own material rather than from general knowledge, and editable in place.
Work in one European language, read source material in another, deliver in a third. Translation is not a separate step; it is how the assistant reads and writes by default.
Single sign-on, role-based access, audit trails and deployment alongside the systems your organisation already runs — so the assistant sits inside your governance instead of beside it.
The product, the people building it and the places it runs.
Start with a single professional seat, or with an integration project — both begin with the same conversation.
The assistant for one person's working day: research, reading, drafting.
Shared material and shared method for a department that works on the same files.
The assistant deployed inside your environment and connected to your systems.
For organisations whose vocabulary is not the general one — legal, industrial, public sector, scientific.
Scope and commercial terms are set after a short discovery call, against your actual use case rather than a list.
Send the enquiry form with what you want the assistant to take off your team's hands. One paragraph is enough to start from.
We talk through the material involved, who would use it, and where your data is allowed to live. If the honest answer is that you do not need us for this, we say so there.
A limited deployment with a real set of your files and a small group of users, measured against criteria you set before it starts — not against a demo script.
Integration with sign-on and access rules, training for the people who will use it daily, and documentation your own administrators can run from afterwards.
Ask a compliance officer about AI and the first question is never about quality. It is: what leaves our building, who processes it, under whose law, and can we show that in an audit. A great model that cannot answer those four questions is, for a regulated organisation, not a usable model.
Sovereign AI is the short name for taking those questions as design constraints rather than as paperwork at the end. It means being able to state where processing happens, to deploy inside infrastructure the customer controls when the use case demands it, and to keep a customer's material out of anything that trains a general model. It also means being able to hand over a written description of all of this to someone whose job is to be unconvinced.
None of this makes the assistant slower to use. It changes who is allowed to use it — which, in most European organisations, is the part that decides whether a tool ever gets past a pilot.
Send a short description of the work and we will come back with questions about it — and an honest view of whether the assistant fits.
Thank you — someone from the team will get in touch shortly to arrange a scoping conversation.
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