AI automation services
Our AI automation services take repetitive work off your operations, from reading documents to routing approvals and closing the books. As an AI automation agency, we keep your team in control of every decision that matters and give back the hours that used to go on copying data between systems.
Automation services
Rules or AI for each step.
Most automations are a mix. We decide per step whether a fixed rule is enough or a language model is needed, and keep a person on the decisions that carry weight. Rules cost less to run and are easier to check, so a model only goes where a rule cannot do the job.
| If the step | We use | Why |
|---|---|---|
| Works on structured data and follows a condition | A fixed rule | Same result every time, and a clear error when it fails |
| Has to read an email, a PDF or a scan | A language model, with rules checking its output | A rule cannot read free text, but it can verify what the model returns |
| Needs judgement across several systems | An AI agent with approval steps | Each case takes a different path |
| Carries legal or financial weight | A person, with the automation preparing the facts | Someone has to be accountable for the decision |
RPA is a separate question. It clicks through screens the way a person would and breaks when a layout changes, so we use an API wherever a system documents one. Our guide on how to automate business processes walks through the choice step by step.
How we work on it
Start with the process
We map how the work really happens, including the exceptions, before choosing tools, because automating a process with gaps repeats those gaps faster.
People sign off on what matters
Payments, rejections and anything with legal weight pass through a person. The automation prepares the decision and records who made it.
Measure before switching over
We compare automated results with the manual process on real volume, so you decide on go-live with numbers in front of you.
Built to be maintained
Clear logs, alerts and documentation mean your team can see what ran, what failed and why, long after we hand over.
Related work
- Reference build
AI invoice approval agent that cites the policy clause behind every decision
100%of automatic decisions cite a policy clause- AI agents
- Human in the loop
- RAG
- Healthcare
Document parsing for patient referrals, with 80% less manual intake work
80%less manual intake work- Document parsing
- AI agents
- Claude
- Logistics
How AI agents saved a freight forwarder €400k a year on shipment exceptions
€400ksaved a year- AI agents
- LangGraph
- Workflow automation
Built with
All technologiesFurther reading
How to automate your business processes
How to automate business processes one at a time: pick a repetitive process, measure it, decide between rules and AI for each step, and run it alongside your team before it takes over.
10 min read
PDF data extraction with LLMs: invoices and forms
How to use LLMs for PDF data extraction: turning invoices and forms into reliable structured data with schemas, validation and a review queue for the cases that need a person.
5 min read
Human-in-the-loop AI agents: design patterns for real operations
How we keep a human in the loop when AI agents run on live systems: approval checkpoints, confidence thresholds, tool permissions, audit trails and fallbacks.
5 min read
Common questions
Frequent, time-consuming work with clear rules and a measurable error cost. Invoice intake, approvals, reconciliation and request triage often come out on top. We rank candidates by effort saved and build difficulty during discovery, and our guide on how to automate business processes sets out the method if you want to start on your own.
Classic RPA clicks through screens and breaks when a layout changes. We prefer APIs and direct integrations, and use language models for the unstructured parts such as emails and documents. Where screen automation is the only route, we use it deliberately and monitor it closely.
A single well-defined workflow moves much faster than a department-wide programme. We agree the timeline after discovery, once we know the systems, data quality and number of exceptions involved.
We run workflows in EU regions or your own infrastructure, use least-privilege access to each system and log every run. For sensitive data we can use EU-hosted or self-hosted models, and we agree the data processing terms before work starts.
In practice it removes the copying, checking and chasing, and moves people towards exceptions, customers and judgement calls. How you use the freed capacity is your decision, and we design the workflows around the team you have.
Ask who maps the process with your team, how they decide between rules and AI for each step, what happens when a run fails and who owns the workflows and code afterwards. Check that they run what they build, or hand it over with documentation, alerts and a named owner per workflow.