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AI flows that run reliably, day after day

Your own data, your existing systems and the right models, connected in a pipeline you can trust.

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Your system

From AI demo to daily process

A demo with ChatGPT is made in an afternoon. The real work starts when that one AI step has to become part of a process: documents that come in, get read, checked, enriched and passed on, every day again and without anyone sitting next to it.

A typical pipeline looks like this. A document comes in, a fast and cheap model classifies it, a stronger model pulls the right data out of it, a check step tests the result against your own rules and only then does it go into your system. Anything the pipeline is unsure about goes straight to a colleague for review.

We also connect those flows to your own knowledge. Through retrieval, a model bases every answer on your documents, price lists or contracts. Which model we put on each step, Claude, GPT or an open model, we choose on what measurably performs best there, at the lowest cost.

How we work

From process to reliable pipeline

Real examples set the quality bar. After that the numbers prove when the AI flow is ready for daily use.

  1. Week 1

    We pick the process

    We start small and measurable: one process that costs a lot of time or produces a lot of errors today.

  2. Week 1–2

    Test set & scope

    Together we put a test set together of real examples with the right answer alongside. That set becomes the bar for every version of the pipeline.

  3. Week 2–6

    Build & measure

    Every step is built, measured and sharpened. You see exactly how well each step performs and what a change delivers.

  4. Week 6–8

    Shadow mode & launch

    The pipeline first runs alongside your existing way of working. Only when the numbers are right does it really take the work over.

  5. After that

    Monitoring

    Logging and cost monitoring stay on, per run and per model, so you know at any moment what the pipeline is doing.

Every AI run visible and checkable

A pipeline running in production, connected to your systems, with a dashboard where you follow every run: what came in, what each model decided, what went to a human and what it cost. Logging and cost monitoring are in there as standard, along with error handling that catches problems cleanly.

Everything runs in your environment and your data is never used to train models. For anyone working with sensitive data, we document the whole flow. That doubles as a solid basis for your obligations under the AI Act.

What is in your AI pipeline

This comes as standard in every pipeline we put into production.

  • Pipeline from prototype to production
  • Connection to your systems, data and APIs
  • Test set that measures the quality objectively
  • Logging and monitoring of every step
  • A human in the loop for uncertain cases
  • Cost monitoring per run and per model
  • Retrieval over your own documents
  • Documentation, including for the AI Act

In practice

AI flows that deliver measurable value

All cases
A K&C Marine Contractors diver at work in the port of Zeebrugge

K&C Marine Contractors

Maritime · 2026

Underwater inspection reports done before the diver surfaces

½ day of reporting per inspection cut

A De Greef Spoorkranen rail crane on site

De Greef Spoorkranen

Rail infrastructure · 2026

A custom ERP for rail cranes, sites and work orders

1 platform for every resource

Questions about ai orchestration

What is AI orchestration exactly?

AI orchestration is building flows where several AI steps, models and systems work together on one task, for example a document that gets read, checked and booked automatically. What sets it apart from a standalone chatbot is the reliability: every step is measured, logged and checked.

Which AI models do you use?

We work with Claude, GPT and open models, and pick per step the model that measurably performs best at the lowest cost. We are tied to no vendor, so if a better or cheaper model shows up tomorrow, we simply swap that step over.

What happens to our data?

Your data stays in your environment and is never used to train models. We work with data processing agreements and can choose European hosting where that matters. For sensitive flows we document exactly which data passes where.

Read next

5 min read

Implementing AI: from pilot to daily use

Most AI pilots stall before they deliver anything. Here is how to roll AI out properly: a data check, integration into the daily workflow, measurable gains.

Ready to start?

Which AI process do you want running reliably?

Bring one recurring process and a few real examples. Within 24 hours you get a concrete first scope and a price indication.

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