AI Agents for the EU Mid-Market

Where in your operations
are you losing hours?

Pick the situation closest to yours. We will show you what an agent actually does — not a slide about what it could theoretically do.

Automate client intake end-to-end

An intake agent handles form submission, validates required fields against your criteria, enriches the lead from company registries, routes it to the right team member, and drafts the first response email — all before anyone opens their inbox.

Typical setup: 3–4 weeks to production
Integrates with your existing CRM — we do not ask you to switch
Human review step included by default — agent does the prep, not the decision

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Fix the handoff layer with coordination agents

When a task moves between teams — sales to delivery, delivery to support — something always falls through. A coordination agent watches the state of each item, pings the right person at the right moment, and writes the handoff summary so no one needs to dig through a Slack thread.

Works with Notion, Linear, Jira, or plain spreadsheets
No process reengineering required — agent fits existing flow
Audit log of every action the agent took — readable by humans

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Build a data retrieval agent across fragmented systems

Your team spends 30 minutes per request pulling data from three different tools, normalising it, and formatting a report. A retrieval agent does that fetch-and-format loop in seconds, on demand, and always returns structured output your downstream tools can read.

Connects to REST APIs, databases, and file exports
Schema agreed upfront — outputs never change format without notice
GDPR data handling included in scoping phase

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A failed pilot is a useful map — here is what to do with it

Most AI pilots fail for one of three reasons: the scope was too broad, there was no clear success metric, or the output was never connected to anything real. We do a two-hour post-mortem on your previous attempt and give you a specific go/no-go on whether a revised scope is viable.

Scoping session is a paid engagement — no free consulting in disguise
We will tell you if your use case is not ready — saves everyone time
Deliverable: a written scope doc you can take elsewhere if needed

Request a scoping session

Agents deployed

Client teams

0–4 wk

Typical time to production

Vendor lock-ins introduced

Our work

Four types of agents. Built to run, not to demo.

Workflow agents

Agents that replace multi-step human processes: intake, routing, follow-up, reporting. They know when to act and when to hand off to a person.

See scope

Data retrieval agents

Fetch, normalise, and format data from multiple sources on demand. Output is structured and consistent — no more ad hoc reports built in spreadsheets at 5pm.

See scope

Integration agents

Connect systems that were never designed to talk to each other. The agent handles the translation layer, error recovery, and retry logic so your team does not have to.

See scope

How we start

We map your stack before we write a line of agent code

Every engagement starts with a stack scan: what tools you use, where data moves, where it stops moving, and where a human is doing something a machine should handle.

Request a stack scan

Typical stack assessment output

CRM connection HubSpot — API accessible

Data warehouse Postgres — schema mapped

Support tool Zendesk — scanning…

Finance export Manual CSV — flag for agent

Auth / GDPR scope EU data processing — confirmed

Common questions

Things people ask before engaging

What exactly is an AI agent, as distinct from a chatbot or automation?

A chatbot responds to a message. An automation runs the same fixed steps every time. An agent does neither: it perceives a situation, decides which tools or actions to use, takes those actions, and adjusts based on what it gets back. The key difference is that an agent can handle decision branches a rule-based system cannot. It is still bounded — we define what it can and cannot do — but within those bounds it acts, not just reacts.

Do we need to replace our current tools to work with you?

No. Every agent we build integrates with your existing stack. We treat tool migration as a separate conversation from agent deployment — mixing them in the same project is one of the most reliable ways to make both fail. If you have a tool that genuinely cannot support an agent integration, we will tell you that in the scoping phase, not three weeks into development.

How do you handle GDPR and EU data residency requirements?

It is part of scoping, not an afterthought. We document which data the agent touches, where it is processed, and what the lawful basis is. For clients requiring EU data residency, we use EU-based inference providers or self-hosted models depending on the use case. We are not lawyers — for DPA agreements we recommend you involve your legal team — but we do not hand you a technical system that creates compliance problems you did not have before.

What does a typical engagement cost and how long does it take?

Project-based, starting from €4,500 for a well-scoped single-workflow agent. Most first engagements land in the €6,000–€12,000 range depending on the number of integrations and the complexity of decision logic required. Time to a working agent in production is typically 3–4 weeks. We do not do open-ended retainers for initial builds — you get a defined scope, a fixed deliverable, and a handover document.

What happens after the agent is delivered?

You get full ownership of the code and infrastructure. Every delivery includes a runbook: how to monitor the agent, how to adjust thresholds, and what to do when it hits an edge case it cannot handle. We offer a 60-day support window after go-live for bug fixes and minor adjustments. Beyond that, we scope ongoing work separately — so you are never in a position where maintenance is the only way to talk to us.

Field Notes