SERVICESAI AGENTSAI agents that finish the work
An agent that acts in your email, CRM or ERP by itself, within limits set in advance. A person decides where it matters, and every action is logged.
ISO 9001 & 27001
The agent does the work in your own systems. A person decides where it matters. Meet the AI employees →
What is an AI agent?
An AI agent is software that reads information, makes a decision within limits set in advance and then carries out an action itself in existing systems, such as matching an invoice to an order or updating a ticket. A chatbot only answers, and ordinary automation only follows fixed rules; an agent can also deal with free text and documents that differ every time. When you have an AI agent built by Your Tech Club, you get an agent that works in your organisation's own systems, logs every action and puts decisions with impact to an employee first.
Built for clients including Tata Steel, M&G Investments, Digistore24, Toppers in Concert, Bar-le-Duc and Loogman
Three situations we often see at organisations that already use AI but don't yet have an agent that finishes work by itself.
The chatbot answers, but doesn't do anything
A customer gets an answer, but closing the ticket, updating the order or updating the CRM is still done by hand.
Automation breaks on every exception
A workflow with fixed rules works until the input is different: an invoice in another format, an email in free text. Then it all lands back on a colleague's desk.
Nobody sees what the AI decided
Actions happen without logging and without a moment where someone can check. If something goes wrong, there's no way to trace why.
AI agent or automation: what do you need?
Not every process needs an agent. If the rules are fixed and the input is clean, an ordinary integration or workflow is faster, cheaper and easier to manage. We say so even when you come to us asking for an agent.
An agent becomes interesting as soon as language or judgement is involved: a mailbox full of questions in free text, packing slips that differ per supplier, a request that has to be understood before anything can be done with it. Often the best solution is a combination: automation for the fixed steps, an agent for the part that requires reading and deciding.
- Fixed rules, clean input: automation
- Free text or varying documents: an agent
- Often a combination of both
How do you stay in control of what an AI agent does?
An agent that acts on its own needs limits. For each task we set out in advance what it may do by itself, what goes to an employee for approval first and when it hands the work over. Labelling an email is often fine to do alone; sending a quote or preparing a payment goes past a person.
Every action is logged: what the agent saw, what it decided and what it did. So you can always trace afterwards why something happened. In the same step we determine which risk category of the EU AI Act the application falls under, so you know which requirements apply before anything goes live.
- What the agent may do by itself, set per task
- Human approval where it matters
- Every action logged and traceable
AI agent development for the systems you already use
An agent is only useful if it can do what an employee does: read an email, look up a customer in the CRM, update an order in the ERP, prepare a document in the accounts. That's why the build starts with access: which systems, with which permissions, and what the agent may read and change there.
We aren't tied to a single language model. For each use case we choose a model via an API or an open model running within the EU, depending on the data and the requirements. The agent, the instructions and the integrations remain yours.
- Email, CRM, ERP, planning and document management
- Permissions per system agreed in advance
- Model-agnostic: an API or an open model in the EU
An agent that works in production, with the control that comes with it. Not a demo that stalls after a month.
We start with the process
First what the work costs now and where it gets stuck. Only then the question of whether an agent is the right answer.
Works in your own systems
Email, CRM, ERP or planning: the agent acts where the work happens, with permissions set per system.
A person decides where it matters
For each task it is set out what the agent may do by itself, what goes past an employee first and when it hands over.
Every action logged
What the agent saw, decided and did can be traced afterwards. Even when nothing went wrong.
Yours, not a supplier's
The agent, the instructions and the integrations remain yours. We are model-agnostic, so you aren't tied to a single provider.
We stay until it works
After go-live we monitor, fine-tune and expand. That's the difference between a builder and an implementation partner.
- projects delivered
- returning clients
- from 127 reviews
- ISO27001 and 9001 certified
Three ways to bring AI into your organisation, side by side.
- Works in your own systems, such as ERP, CRM and emailStandalone AI tool: partly · AI freelancer: yes
- ISO 9001 and ISO 27001 certifiedStandalone AI tool: varies · AI freelancer: rarely
- Starts with the process, not the technologyStandalone AI tool: no · AI freelancer: varies
- Tells you when AI isn't the answerStandalone AI tool: no · AI freelancer: varies
- A person decides where needed, with loggingStandalone AI tool: partly · AI freelancer: varies
- Strategy, design, software and AI in one teamStandalone AI tool: no · AI freelancer: no
- Management and further development after go-liveStandalone AI tool: via subscription · AI freelancer: varies
- Dedicated contacts, office in AmsterdamStandalone AI tool: no · AI freelancer: varies
Five steps. How long it takes depends on the number of systems and exceptions, not on a standard schedule.
- 01
Define the process and the limits
Which work does the agent take over, what does it cost now, and where does a person keep deciding. This is also where we determine the risk category under the EU AI Act.
- 02
Foundation and access
Access to the systems, the data the agent may use, the integrations and the logging. And an owner for each step.
- 03
Build and test on real cases
We build the agent and test it on examples from your own day-to-day work, including the exceptions, before it's allowed to do anything.
- 04
Run alongside with approval
The agent runs alongside one team. At first everything goes past an employee; what demonstrably goes well may then run on its own.
- 05
Go live and expand
Live with monitoring. If it works, more tasks or processes are added on the same foundation.
After go-live, the agent stays under supervision
- MonitoringWe track errors, exceptions and costs per task, and see it when something deviates.
- EvaluationNew instructions or a different model are tested first on the same examples from your own work.
- Access and loggingPermissions follow the roles in your organisation, and logging stays on, even when everything goes well.
- ExpandingNew tasks are added as soon as they demonstrably add value.
No account manager. Your first conversation is with someone who does the work, often with one of the founders. After that you work with a dedicated team from our office in Amsterdam.
- Office on the Duivendrechtsekade in Amsterdam
- ISO 9001 and ISO 27001, certified by Kiwa
- 500 projects delivered
- 4.8 from 127 reviews
What is the difference between an AI agent and a chatbot?
A chatbot answers a question. An AI agent carries out tasks in your systems: it reads a customer email, looks up the order in the ERP, updates it and prepares the reply. A chatbot talks, an agent finishes the work, within limits you set in advance.
AI agent or ordinary automation: when do you choose which?
If the rules are fixed and the input is clean, ordinary automation is faster, cheaper and easier to manage. An agent only makes sense when language or judgement is involved, such as email in free text or documents that differ per sender. Often the best solution is a combination, and if an integration is enough, we say so.
What does AI agent development cost?
It mainly depends on three things: how many systems the agent has to work in, how many exceptions the process has and where a person needs to be involved in decisions. We build agents as part of AI implementation, for a monthly fee that depends on scope and pace. If you don't know yet which process delivers the most, start with the AI Opportunity Audit: a fixed price, known up front.
Which systems can an AI agent work in?
The systems where the work already happens: email, CRM, ERP, accounting, planning or document management, through an integration or API. For each system we set out what the agent may read and what it may change. If a system has no usable integration, we first discuss what's needed before we build.
When does a person decide, and is everything logged?
For each task we set out in advance what the agent may do on its own, what goes to an employee for approval first and when it hands the work over. Labelling an email is often fine to do alone; sending a quote goes past a person. Every action is logged: what the agent saw, what it decided and what it did.
Where is our data stored, and what about the GDPR and the EU AI Act?
Which data the agent gets to see is decided in the design, and it is no more than the task requires. For each use case we choose a model via an API or an open model running within the EU, depending on how sensitive the data is. We determine in advance which risk category of the EU AI Act the application falls under. Your Tech Club is ISO 27001 certified by Kiwa; that covers how we work and does not replace an assessment of each application.
Which language model do you use for an AI agent?
We choose per use case. We are model-agnostic: sometimes a model via an API fits, sometimes an open model running within the EU. The choice depends on the quality the task requires, the sensitivity of the data and the cost per task, and you aren't tied to a single provider afterwards.
Do we need an audit before having an AI agent built?
Not if you already know which process the agent should take over; then we start with that process and the systems around it. If you don't know yet where the most value is, start with the AI Opportunity Audit. In five working days it shows what the current work costs, and also whether an agent is the right answer there at all.
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