Topthor AI Hub is a complete AI environment: assistants and agents that work on your documents, your systems and your processes. Every person gets their own workspace, every action runs on their identity and within the permissions you already maintain, and you decide where it runs and which models it may use.
That is the gap Topthor AI Hub is built for. It is one environment for the whole organisation: the skills one team writes are available to every team that needs them, and agents work across departments rather than inside one person's chat.
The AI Hub is not priced per seat. You pay for the environment, the work of fitting it to your company and its upkeep. Model usage is visible per person and per team, so you can see what is actually used and what it costs.
So do agents in the AI Hub. One can read from one system, update another and tell the people who need to act, as a single piece of work. The process runs the way it really runs, rather than the way one application sees it.
That is exactly how we work. The environment arrives working, and our skill design team turns the way your business operates into skills and agents, alongside your people. Once it is in, your own team extends it.
Work that nobody had time for starts getting done. The tasks that were always worth doing and never urgent enough - reviews, summaries, checks, first drafts - run on a schedule instead of waiting for a free afternoon.
Institutional memory stops walking out of the door. When someone leaves, what they knew stays searchable, and the way they worked stays encoded as a skill the next person inherits.
Decisions become defensible. Every assessment carries its sources and every step an agent takes is recorded, so a challenge from an auditor, a supervisory body or a rejected applicant has an answer ready.
You get an AI capability without an AI department. The environment arrives working. Your people decide what it does, not how it runs.
Your people become the ones who improve it. After the first deployment, new skills come from the people who need them, without a change request to us.
A working AI platform, delivered ready: identity and permissions, an isolated workspace for every person, assistants and agents, a knowledge base over your own material, model routing, monitoring and cost control, assembled and tested before it reaches you.
Everyone works in their own isolated environment, with their own files, history, tools and limits. Nothing is shared by accident, and one person's work cannot surface in another person's answer.
Where an answer comes from your material, it arrives with a citation pointing at the passage it came from. Where the material is silent, the system says so instead of inventing.
Every component is open source or licensed for free institutional use, and the distributed storage layer is our own. What we add is the part that takes months: choosing what works together, wiring it into one system and keeping it upgradable.
A chat assistant answers from whatever it is shown in one conversation. An agent in the AI Hub has its own workspace: it can open files, query your systems, run the analysis and check the result before anyone reads it. That is the difference between an answer and finished work.
The weekly summary is already built from the systems it depends on, and waiting for the people who need it.
It is read, checked against what it refers to and sent to the right person with a note on what needs attention.
One system is read, another is updated, and the people who need the result are told, as one piece of work.
You state the task; the assistant works out which specialists it needs, hands them the parts and assembles the result. Long work runs unattended, keeps its own progress and picks up where it left off, and work can start on a schedule or when something happens, without anyone asking.
For a board these are one decision, so they sit in one place. None of it depends on people remembering a policy: it is how the environment is put together.
It acts in a named person's name, through the groups your directory already holds, and sees the files, systems and tools that person sees. A tool someone is not entitled to use does not appear to their agent at all.
Who asked, which agent ran, which systems it touched, what it produced and what it cost, recorded against a person and reconstructable afterwards. Budgets are set per user and per team.
On your own hardware, through managed models behind an API, or a line drawn between the two, category by category. The platform enforces the choice and you can change it later without rebuilding anything. We do not build our own language model, deliberately: the ones worth using change every few months, and an environment tied to one of them inherits its ceiling.
Actions with consequences are proposed and wait. The assistant shows what it intends to do; a person approves, edits or stops it. You decide which actions belong in that category.
The trade-off, stated plainly. Not every model is equally fast or equally capable. Public models are usually the strongest; models on your own servers keep more inside, sometimes at the cost of speed or depth. You make that choice per task, rather than once for the whole company.
Regulations such as the EU AI Act and GDPR increasingly ask organisations to show how AI is used, not only to say so. Where data sits, which model saw it, who asked and what the agent did are recorded because the environment is built that way. When an auditor, a supervisory body or a client asks, the evidence already exists. We do not certify your compliance; we make it possible to demonstrate.
The procedures your best people follow - how a submission is assessed, what a good brief contains, which questions come first - become skills the assistant loads when a task needs them and releases afterwards. Staff use them without ever seeing their contents, so the method stays yours even as people come and go. Editing them is a controlled process with review and approval, not somebody quietly changing a prompt.
A capable AI environment with nothing mapped onto it is an efficient way to do nothing in particular. The value appears when it knows how your organisation actually operates. Turning that into skills and agents is the work we call skill design.
Some AI companies hire specialists in one industry and sell that industry their own expertise. We do the opposite. Your people already know how the work is done, better than any outside specialist will learn it. Our part is turning that knowledge into something the environment can run, in whatever business you are in.
Describe how the process runs today, where it goes wrong and what a good result looks like. That description becomes the standard the deployment is accepted against.
Turn it into agents, skills and permissions, and work alongside your team until the result meets that standard.
The skills belong to your people. They can read them, change them and add new ones, while we keep the environment running underneath.
An organisation running a multi-year programme with several partner institutions. A library of strategies, resolutions, studies and reports spread across a shared drive. A small team, no AI specialists, and every output open to challenge from outside. Three modules were deployed on top of the same platform.
Staff ask a question in plain language and get a synthesis drawn from internal documents, each claim linked to the passage it came from. Optionally the search can be widened to a short list of approved external sources - never to the open internet by default.
What changed: preparing background for a decision stopped being a week of reading. New staff stopped depending on whoever remembers where things are.
A proposal is submitted, the system classifies it and proposes which criteria and weights apply - then stops and waits for approval. After the go-ahead, each criterion is scored on a fixed scale, and every score must be justified with a quotation from an approved reference document. A validation pass checks the scores against those sources before anyone sees the result.
What changed: evaluations became comparable to each other and defensible to a third party. The report also lists what could not be assessed, because the submission did not say - which is often the more useful half.
For a defined challenge, the assistant produces a set of draft concepts, then passes each one through the evaluation module, improves the weak areas and re-scores. Only variants that actually improve move forward; the rejected ones are kept so the same dead end is not proposed twice.
What changed: the workshop stopped starting from a blank page - and the ideas arrived already measured against the same criteria they would be judged by later.
Answers drawn from your material come with citations attached. A separate check compares the output against those sources before it is shown, and corrects it if they disagree. Where the material is silent, it says so.
People use what is already part of how they work. That is why a deployment starts from your processes rather than a training session, and why usage per team is visible from the first week, so anything nobody opens gets fixed or dropped early.
You receive the source code in your own private repository, including the gateways and integrations we built for you, and you keep the right to use it after our work together ends. The components underneath are open source. Ending the relationship means losing our help, not losing the system.
In a private repository you control, including everything written specifically for you.
Written in a form your people can read, change and keep using.
Where you chose to keep it, in formats you can open without us.
A good first process repeats, draws on documents and at least one system, and today waits for someone who is always busy.
About one process: how it runs today, where it stalls and what a good result would be.
With an agreed scope and an agreed definition of success. What you pay is credited in full against the deployment that follows.
Once you have seen it work on your own material, rather than in a demonstration.
The fastest way to judge this is on your own work. Thirty minutes, a sample of your material, and one task your team keeps postponing.
Scope your pilotPaid pilot · one or two processes · scope agreed on the first call