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Autonomous AI agentA digital co-worker that finishes the job Experience the agentThe experience in a two-minute scroll AI GovernanceEU AI Act-ready, with a living register AI Agent PlaybookBuild it yourself with copy-paste templates For installersQuotes, scheduling and client communication Interim AI leadProgramme steering, hands-on on site
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Product page · Done-for-you AI agent

A digital co-worker that works alongside you.

Not a tool, but a colleague. Give it work in plain English. It plans, writes, analyses and logs every step. Active 24/7, within your permissions, set up in four working days.

Book an intro call → See the use cases first
Built withAnthropicOpenAIn8n Enterprise expertise from an AI Portfolio Lead at a top-3 bank
What the agent is & what it isn't

AI in operations, not in a slide.

A dedicated task owner for the work that disrupts every team: research, email, status, quotes. It gets clear permissions, and stays within those lines.

01 — What it does

Take over work, not just suggest it

The agent picks up tasks itself: it reads documents, connects systems, drafts, summarises and delivers in your template.

  • Search & summarise documents
  • Draft quotes and reports
  • Triage email, prepare replies
  • Track actions and deadlines
02 — What it doesn't do

No black box, no blank cheque

It makes no decisions that go to clients or the board without review. It does nothing outside its permission card, and it says when it doesn't know something.

  • No client communication without approval
  • No "it seems like", sources are named
  • No work outside its permissions
  • No unseen payments, contracts or decisions
03 — How you instruct it

Plain English. No prompt engineering.

You tell it what you want the way you would tell a new colleague. We translate that into prompts, tools and limits that pass the review.

  • One conversation replaces 10 tools
  • Instructions are version-tracked
  • Output follows your house style
  • You always see what it was thinking
How it works in practice

Plain English in. Structured action out.

One instruction, four steps, one checkpoint. What comes out is not a chat, it's work you can send.

1

You give the brief

In plain language. One-off, or as a recurring routine ("every Monday 09:00").

2

The agent gathers context

From your documents, email, CRM, scheduling, only where it has access according to its permission card.

3

It does the work

Drafts, checks, structures. Flags uncertainties and sources.

4

You approve

One click. Or: adjust and run it again. Every step stays logged for audit.

Use cases

What it can take off your plate today.

Five tasks we put live in the first working week. Start with one, scale up when the numbers add up.

01 · Flagship

Quote agent — from briefing to draft in minutes.

The agent reads the request, retrieves the client data and calculation, and drafts a quote in your house style. You check and send.

Now — manual
45 min

Per quote, by your best people.

With the agent
3 min

Draft ready, you approve.

02

Email triage

Inbox sorted, urgent on top, draft reply ready, before you've had your coffee.

→ 40+ emails/hr
03

Virtual PMO

Status, risks and action lists from your project documents, weekly, on a single page.

→ steering info Mon 09:00
04

Market & sector research

The agent searches publications, regulator bulletins and industry data; delivers a well-founded summary with sources cited.

→ weeks of research in hours
05

Meeting action lists

Notes, action points with owner and deadline, followed up automatically until they're done.

→ nothing slips through
Who it's for

For teams where repetitive work holds growth back.

SMEs and scale-ups from 10 to 500 employees. One pilot team first, scale up when it works.

Operations & back office

Email, scheduling, client data

Repetitive work that's well structured but recurs every day, the first, easiest win.

Sales & advisory

Quotes, lead research

Preparatory work in minutes, so your people can have the conversations.

Projects & research

Status, reporting, sector scan

One agent as virtual PMO and research assistant for leaders who need an overview.

Trust — governance

Secure enough for a top-3 bank.

Done-for-you, but never a black box. The agent gets a permission card, a log and an owner, before it touches anything.

Three levels of permission — defined per task.

Not one big "AI can do anything" button. For each task: may the agent act autonomously, does it ask for review, or is it simply forbidden? Per use case, per role, per dataset, logged in the AI register.

TaskModeExample
Summarise a document— autonomousWeekly status ready to read
Quote draft version— with reviewA human approves before sending
Reply to a client email— with reviewDraft ready, you send it
Payment, contract, pricing decision— forbiddenOutside the agent's permissions
Permissions
Per task, per role, recorded in the register
Logging — always
Inputs, reasoning, output traceable
Owner
One human accountable per agent
EU AI Act
Documentation that covers the law

Read the full governance approach →

How we work

Measure → build → scale.

Four working days to a live agent. Only then scale up, when the numbers add up.

1
Day 1 — measure

Audit & permission card

We map out which task costs a lot of time, which data it touches, and which permissions are safe.

→ business case + scope
2
Day 2–4 — build

Agent live

One task, integrated into your existing tools, with logging and KPIs from the very first run.

→ working agent + measurement plan
3
From week 2

Scale on proof

The next task only when the first one pays off. Same register, same governance, no annual contracts without proof.

→ rollout on proof
Proof

An agent that passes the review of a top-3 bank.

Case · Finance · 2026

Agent workflows inside a heavily regulated environment.

A top-3 Dutch bank has been running a pilot with a FlowBaas agent since Q2 2026. The goal: to prove that agent-driven workflows can be deployed safely and verifiably, within the existing compliance and security frameworks, with human review on all output that goes to clients or decision-making.

Name & figures under NDA — ask for the full case in a call
Frequently asked questions

The questions we hear in every intro call.

How do I know the agent won't do anything stupid?

It gets a permission card: defined per task what it may do autonomously, what goes past you first, and what is forbidden. Nothing lands outside those lines, and everything it does is logged.

What if it makes a mistake? Who is responsible?

Per agent, ownership sits with a human inside your organisation, not with us, not with the vendor. We set things up so that mistakes become visible before anything goes towards a client or a decision. The EU AI Act requires this; we build as if it were already mandatory.

Does our data stay within our environment?

By default, yes. Which models we use, which data they touch and what we can retain we define per pilot, before we build. No surprises afterwards.

What does this cost and what does the pilot look like?

Four working days to live, with one concrete use case and a measured result. Fixed price, no annual contract. Only scale up once the first agent pays off. We discuss the figures in the intro call, depending on scope and integrations.

We already have ChatGPT / a copilot. Is this different?

Yes. A chatbot waits until you ask something. An agent picks up tasks itself, connects with your systems, checks its own work and delivers. The difference is "tool" versus "colleague".

Start

Ready to set up your first digital co-worker?

No off-the-shelf packages. A 30-minute intro call in which we honestly tell you whether we can add value, and if not, we point you elsewhere.

Book an intro call → Send an email
Usually responds within 1 working day. No obligation, no sales funnel.