Agentic transformation of finance
Your own people learn to use artificial intelligence on the work they already do. Each person delivers from the start, and the knowledge stays in the company.
Agentic transformation of finance
Your own people learn to use artificial intelligence on the work they already do. Each person delivers from the start, and the knowledge stays in the company.
We teach your team to use artificial intelligence on the work they already do every day. We do not come in to automate the area from outside.
Repetitive work is automated at the start, but it is not what wears the team down. What wears them down is the report that comes from six sources, the exception resolved by asking someone, the criterion that lives in one person’s head. That is where we come in.
Nobody hands the process to a black box. The agent works inside a harness: written limits, a record of what it did, and a way to undo it. The person stays in charge. And what is learned ends up documented in a company space, not in the head of whoever built it.
In the end the area has its process written down: where each thing goes, who decides what, and how long it takes. With that in view, what looked impossible to automate ends up automated.
It works in any industry. The financial processes of companies resemble each other far more than their products do.
The engagement, phase by phase
We observe the real work of the area
We walk through what the area actually does, role by role, and we characterise and measure it. Repetitive work is automated at the start; work that needs human judgement is marked to be automated step by step while the person carries on with their day, because the AI keeps extracting the patterns until it finds the agentic path.
When it ends, you haveThe work of the area inventoried, with how long each thing takes today, and the first automations already running.
Your team learns to use it
Training on each person’s real work, not on examples, and with the tools your company already has. From day one we agree what information may go in and what may not.
When it ends, you haveEvery participant using artificial intelligence on a real task of their job, with the information-handling rules already agreed.
Each person automates their own work
The people in the area automate the tasks they already do. The agent works inside a harness: written limits, a record of what it decided, and a way to undo it.
When it ends, you haveAutomations running on the real work of the area, each with its limits and its record.
The knowledge stays
Every automated task ends up documented in a company space: what it does, why it was decided that way and what was tested. It does not live in the head of whoever built it.
When it ends, you haveA company space per topic, which a colleague can pick up without starting from scratch.
It gets measured, and discussed with leadership
Every automation is compared against how long the task used to take: response, delivery, volume and cost of use. It is there to hold a conversation with leadership backed by data.
When it ends, you haveIndicators with their baseline and periodic measurement, and a report to leadership with the results.
What the company gains
The times get shorter
Tasks that take hours today are resolved in a fraction of that, with the person reviewing instead of executing.
How you noticeResponse and delivery time, against week one.
Control stays inside the area
Whoever knows the process is the one who automates it and fixes it. No ticket to open, no vendor to wait for.
How you noticeHow long the automation takes to reflect a change in the process.
You decide who sees it
What the artificial intelligence learns from each person’s work is kept. It can live only in their team or serve the whole area, and you draw that line.
How you noticeWho sees what is decided by the permissions your company already administers. There is no new access system.
Nothing runs blind
The agent works within written limits, records what it decided, and can be undone. No decisions nobody can explain.
How you noticeEvery automated decision has a record of who defined it and on which version.
It improves daily, it does not age
The person adjusts their automation as the odd cases show up. The opposite of a system that freezes the day it is handed over.
How you noticeAdjustments made by the team itself since go-live.
Without being tied to us
The work is done with standard market tools, the same ones your company will still have when we are finished.
How you noticeThe area keeps extending its automations without us.
The area ends up with its process written down
When every person automates their work with the same method, how the whole area works appears in writing. With that in view, what looked impossible to automate ends up automated.
How you noticeThe process documented end to end, with the points where it stalls today and why.
How you will know it works
The measurement
Before touching anything we measure how long each task takes today. Without that figure the phase does not start, because afterwards there would be nothing to compare against.
How to verify itThe baseline is taken in the first phase and handed to you. Every improvement is compared against it, not against an impression.
The translation to business
Every operational improvement is carried through to the number it belongs to: revenue, volume or cost. If one reaches none of them, it is reported all the same, stating that it did not.
How to verify itThe dashboard keeps both series, operational and business, with their full history. It is yours from day one.