Service
AI agents: hand over routine work reliably
We develop agent workflows and automations that handle multi-step tasks on their own – combining data, creating records, preparing drafts. With clearly defined points where a human reviews and approves.
Discuss a processWhere automation reaches its limits today
Conventional automation works well as long as data is structured and processes are always the same. Day-to-day reality often looks different: enquiries arrive as free-form e-mails, information is spread across several systems, and every special case has an exception. In the end, the work lands back with employees who type in data, reconcile systems and create records by hand.
AI agents close this gap: they understand unstructured input, decide which step is needed next and use your existing systems through their interfaces. What matters is not maximum autonomy but reliability – which is why we combine AI where it brings advantages with hard-coded rules where results must be exact.
What we build for you
Agent workflows
Multi-step processes in which an agent gathers information, queries systems, prepares decisions and triggers follow-up steps – with a traceable log of every step.
Agent-based processes
An agent handles processes with unstructured input such as e-mails, PDFs or free text on its own; fixed rules apply where results must be exact. For purely rule-based processes, our process automation is the better choice.
Internal assistants
Assistants for sales, customer service or administration that access your systems and knowledge – and only see what the respective person is allowed to see.
Connecting your systems
We connect agents securely to ERP, CRM, e-mail, ticketing systems and databases – via clean interfaces with strictly limited permissions.
Typical use cases
Order intake
Orders from e-mails and PDFs are read, checked against customer data and stock levels and created as an order in the ERP – unclear cases go to the team for review.
Incoming invoices
Incoming invoices are captured, matched against the purchase order and delivery note and prepared for approval.
Customer service
Requests are categorised, enriched with customer data and forwarded to the responsible person together with a draft reply.
Research and reports
Information from several sources is compiled regularly and prepared as a report – for example for sales, purchasing or management.
Control instead of a black box
An agent that works independently in your systems needs clear boundaries. We build in security and traceability from the start.
- Least privilege: the agent may only perform the actions needed for its task
- Approval points: a human confirms critical steps such as payments, contract changes or customer communication
- A complete log of every decision and every action
- Privacy-compliant processing with providers in the EU or models in your own infrastructure
- Clean error handling: when in doubt, the agent hands over to a human instead of guessing
How we work
1. Understand the process
We analyse the process, its exceptions and the systems involved – and check whether conventional automation is enough or whether an agent adds real value.
2. Define approval points
Together we define what the agent may do on its own and where a human reviews and decides.
3. Build with real cases
We develop the workflow and test it with real cases from your daily business before it goes live.
4. Operation and expansion
In operation, we monitor quality, cost and error rates and gradually extend the agent to further tasks.
Frequently asked questions about AI agents
What is the difference between an AI agent and conventional automation?
Conventional automation follows hard-coded rules and needs structured data. An AI agent can understand unstructured input such as e-mails and decide for itself which step is needed next. In practice we combine both: rules where results must be exact, and AI where flexibility is required.
How do you prevent an agent from making mistakes?
Mistakes cannot be ruled out completely – so we limit their impact. The agent only gets the permissions it needs, a human confirms critical steps, and every action is logged. Before going live, we test with real cases and measure the error rate.
Which systems can agents work with?
With almost any system that offers an interface – for example ERP, CRM, e-mail, ticketing systems, databases or file storage. If there is no interface, we look at alternatives such as data exports or a custom integration.
Are AI agents covered by the EU AI Act?
That depends on the purpose. Most automations in sales, purchasing or administration are not considered high-risk AI. It can be different if an agent helps decide on job applications or creditworthiness, for example. We classify every use case in advance.
How quickly does an AI agent pay off?
That depends on how often the process occurs and how much manual work it costs today. We estimate the benefit during the analysis and start with a clearly defined process so that the effect can be measured early.
Which process costs your team the most time?
Describe a process that requires a lot of manual work today. We will show you whether and how it can be automated.