Agents that work together
An agent has a name, a job, a scope and a set of tools, and hands work to other agents under the same rules as your people.
Let’s talk With AI, the hard part is knowing where to start. Start too big, and high expectations meet a sceptical team. We start small, with a handful of processes where AI pays back first. The platform is ready and safe from day one, so every hour our engineers put in goes into your process and your people. Within weeks, the first team works faster and better with AI, in the systems it already knows.
Shall we find your first process together?The people who own a process get a screen made for it: files in, a check, a document out.
AI workers with a name, a job and a scope, automations on a schedule, and meetings that land in your knowledge.
One platform under everything: sensitive data protected before any AI sees it, every answer grounded in your own sources, and the same rules for your people and your agents.
One example: an order intake. A retailer's order arrives as a PDF. The order lines are read out and shown beside the original, a person checks and corrects them, approves, and the order goes to the ERP as a file it already reads. Nobody types an order twice, and nobody opens a chat to do it.
That is what a skill with its own interface looks like: one button, your files in, a check where a person should check, and a result in the format your systems expect. Every process gets a screen like this, made for the people who run it, and the chat is there for the questions in between.
A skill is a specified piece of work for an AI agent: inputs, outputs, the sources it may use, the format it delivers. It can have its own screen and connect to your own systems, so it fits into the IT you already have. The arithmetic runs in code, in a sealed sandbox with no network, so the same data gives the same answer.
Skills follow the open Agent Skills standard, which makes them reusable and exchangeable. Skills that already exist, and the ones we built before, become building blocks for yours.
A Space is the work area of one team. Its collections hold what the team works from: documents, drives, databases and your own systems, each with an owner who keeps it current. Its policies set what may and may not happen there: which AI the team may use, how personal data is handled, which tools and skills it may use, and who sees what. Set once, applied on every question, for your people and your agents alike.
An agent has a name, a job, a scope and a set of tools, and hands work to other agents under the same rules as your people.
A skill on a schedule or a trigger: the inbox that sorts itself, the review that runs every quarter. Watched and managed with separate permissions.
Aimable Capture joins the call, writes the notes and the actions into a collection, and the meeting becomes something you can ask questions of.
A meeting that produces a document. The agents of the Space work alongside the people at the table: one checks a claim, another writes the decision down, and the document is ready when the meeting ends.
Answers draw on your own curated collections of documents and data, and each one cites where it came from. Click the citation and the document opens at the passage, so a check takes seconds. The best AI in the world gives poor answers on poor sources, which is why collections are curated: one owner, one version, no stale copies.
Word and Excel get an Aimable pane with the same Space, the same collections and the same protection of sensitive data. In Excel it answers with the cells it used. In Word the memo is written in your own template, with the sources attached.
Names, email addresses, ID numbers and other personal data are found and replaced before anything goes to an AI, and put back in the answer your people read. It works on text, files and images, in every language your team works in, without anyone having to think about it. And before a message goes out, the Privacy Check warns and shows what it found, so people see what counts as personal data at the moment it matters. That is how a team learns to handle sensitive data well.
Files, drives, websites, mail, chat and your own systems become collections your AI can draw on. Anything else connects through the API or the open MCP standard: a catalog of seventy ready-made connectors, and your own. Every connection is read-only unless you decide otherwise, and is scoped per Space.
Each Space allows the AI models you choose, and a local model when the data demands it. A change at one provider does not put your work at risk: the same skills run on another model without rebuilding. Your accumulated knowledge never lives inside one vendor.
The Console shows cost per team, per AI and per kind of work, as it happens. A budget per Space or for the whole company is a hard cap, with alerts at the thresholds you set. Bring your own AI keys, or use ours and get one invoice for all of it. The log keeps who asked what, which sources were used and what it cost, for you and for your auditor.
Sensitive data is found and replaced before anything reaches an AI, and restored in the answer.
Every answer cites its sources, and every citation is a link: one click opens the document it was based on, so anyone can check the answer.
Which AI, which sources, which tools, who sees what: set per Space, applied automatically, refused when unclear.
Who asked what, which sources were used, what it cost. For your auditor, and for you.
The platform runs in our EU cloud. For our largest customers we run it in their own cloud. ISO/IEC 27001:2022 certified.
Any AI, switchable per Space. Escrow on the code on request. Your data and your knowledge stay yours.
Thirty minutes with the team: we look at your work, look for the process where AI pays back first, and tell you honestly what it takes. Our engineers connect your systems and set it up with your people, live within weeks.
Shall we find out together?