Most work in a company is ad hoc: questions, small tasks, one-offs. Your AI agents help your team with it. While they work, they write down how they did it, so the job can be done the same way next time. Work that repeats and is well defined is then automated: it runs on its own, and you check the results.
Supplier invoicesDraft entriesPortal invoicesCost taggingBudgetCash-flow forecastMonth-end reportProfitabilityBoard packAsk your dataDue diligenceDemand forecastProcurement planStock differencesContractsPartner emailsAsk the expertSupplier invoicesDraft entriesPortal invoicesCost taggingBudgetCash-flow forecastMonth-end reportProfitabilityBoard packAsk your dataDue diligenceDemand forecastProcurement planStock differencesContractsPartner emailsAsk the expert
This is for you if
A team spends its days retyping, matching and reconciling data your systems already hold.
You tried an assistant. It made the slides and built the budget sheet, but you still had to do the work yourself.
Your specialists answer the same questions from colleagues every week, and their own work waits.
What you get
The result
AI agents working in your systems. Your bookkeeper or controller approves the work instead of doing it, and checks the results of what runs on its own.
Three stages
You ask, the agent helps. The agent prepares, you approve. It runs on its own, and you check the results. If something changes, it comes back to you for approval.
What they need
Your agents need your company dictionary, their own access and rules, and a logbook. Your next agent reuses all of it.
Ownership
You own the repository, the folder of code and settings that holds everything we build for you. You also own your agents and the agent that builds and maintains them. Their tools and data stay in your own cloud, and nothing sits on a platform of ours.
What we do, what you do
We
You
Build your agents for the work, with your data and your company dictionary.
Tell us once what the words mean in your company.
Test your agents and set the limits they work within.
Control access, ask the agents for help, and approve what they prepare.
Turn your corrections into rules, and automate the work that repeats and is written down.
Check the results of what runs on its own.
Worth knowing
Agentic work and automation are different, and the first leads to the second. An AI agent works toward a goal and chooses its own steps, which suits the ad hoc work that fills most days. An automation runs the same steps every time, which suits work that repeats. The link is the logbook: while your agents work, they write down where they found the information, what they learned, what they decided and what they did. When the logbook shows a job repeating the same way, that job can be automated. On its own, a model knows nothing about your company, so your agents also need your company dictionary, a written list of what your words and numbers mean, and tools, access, rules and the limits you set.
Your questions
Can an agent post something nobody checked?
No. Your accounting agent prepares entries as drafts, and your bookkeeper approves them in the accounting system they already work in. Only work that repeats and is written down runs on its own, and you check the results. Money leaving the company, anything going outside it, a commitment and an unclear rule always go to a person.
Does our data leave the company?
Your data, the pipelines and the tools stay in your own cloud project, and you control access. When your agent works, the model provider you chose processes what each step needs, on the terms you have with that provider. Nothing moves into a platform of ours.
What happens when it gets something wrong?
The correction goes into the rules and the dictionary, so the same mistake is not proposed again. If something changes in a job that runs on its own, it comes back to you for approval.
Are we locked in?
No. You own the repository, your agents and the agent that builds and maintains them. You can maintain all of it without us.