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AI Implementation

AI implementation takes a validated use case from first working version to a system running inside your own tools, monitored and handed over. At Crux Digits the same engineers who scoped it build it and take it live.

You've got a model or a promising prototype. Getting it live (reliably, securely, inside your real systems) is where most AI stalls. That's the part we specialise in: taking AI from proof-of-concept to production your team can depend on.

Last updated: 14 September 2026

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In short

AI implementation takes a validated use case from first working version to a system running inside your own tools, monitored and handed over. At Crux Digits that is a €20,000 Production-ready MVP on your data followed by production from €50,000, delivered by the same engineers who scoped it.

What it is

Get AI out of the lab and into production

A working demo and a production system are very different animals. Production means uptime, security, monitoring, versioning and a graceful answer for when the model is wrong. We bridge that gap, wiring AI into your existing stack so it actually gets used, not just admired in a sprint review.

We work with your engineers, not around them. Everything is documented and handed over, so your team can run and extend it long after we've gone.

What's included

A production rollout, end to end

Production deployment

Models packaged, containerised and deployed to your cloud or on-prem: built to scale with real traffic.

Systems integration

AI wired into your CRM, ERP, data warehouse and internal tools through clean, documented APIs.

MLOps & monitoring

Pipelines, versioning and dashboards that catch drift and failures before your users do.

Security & compliance

Access control, data handling and EU AI Act / GDPR alignment baked into the rollout.

Human-in-the-loop

Review steps and fallbacks so people stay in control where the stakes are high.

Handover & enablement

Documentation and training so your team can own and extend it from day one.

How it works

Our AI implementation process: from demo to dependable

Four fixed steps. You see something real at the end of each one.

  1. Two colleagues analysing charts across a desk1

    Assess

    We review the prototype, your stack and the real path to production.

  2. Two engineers working together at their monitors2

    Engineer

    We harden, integrate and instrument the model for real-world load.

  3. A technician with a laptop between server racks3

    Deploy

    We ship to production with monitoring, rollback and security in place.

  4. An engineer at a monitoring workstation wearing a headset4

    Support

    We hand over, train your team and stay available as you scale.

What you walk away with

AI that actually runs in your business

FAQ

Questions, answered

Can you deploy a model we already built?

Yes, bringing an existing model or prototype to production is one of the most common ways teams work with us.

Do you deploy to our cloud or yours?

Yours. We deploy into your AWS, Azure, GCP or on-prem environment so you keep full ownership and control of the system and the data.

What happens when the model gets something wrong?

We design for it: fallbacks, human review and monitoring so mistakes are caught and handled, not shipped silently to your users.

Will our team be able to maintain it?

That's the goal. We document everything and train your engineers, so you're never locked into us to keep it running.

How do you handle EU AI Act and GDPR?

Compliance is part of the rollout: data handling, access control and risk classification are built in, not bolted on afterwards.

Who owns the source code and the model after the project?

You own everything we build. Source code, trained models, pipelines and documentation are fully transferred to you at handover. We retain no rights to what we build for you.

What if we need to change scope mid-project?

We work in short sprints with agreed checkpoints, so there are natural moments to reprioritise. If scope changes materially, we agree a written change note: transparent, no surprises.

Do you provide support and monitoring after launch?

Yes. We include a post-launch monitoring and support period as standard, and can scope a longer SLA if needed. Ask during scoping and we'll price it up front.

Who can we hire to take our AI model or prototype into production in the Netherlands?

Crux Digits is a boutique applied-AI firm based in the Utrecht region, working with clients across the Netherlands and the wider EU. We are a small, senior team that does this hands-on: taking a model or prototype from demo to a working production system inside your real stack. No offshore hand-off, and we work in both English and Dutch.

What does an AI implementation plan from Crux Digits look like, and what does it cost?

Every engagement starts from a written implementation plan: scope, data access, milestones and a fixed price, no open-ended invoices. Cost depends on scope: the Production-ready MVP is a fixed €20,000, larger builds from €50,000. Timelines usually run in weeks rather than months for a first build. We size both after discovery.

Related reading

How to scope an AI proof of concept

Ship a working MVP, not slides

Build vs buy AI software

AI-native software delivery

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Scope

What is included, and what is not

So you can price the gap before a call rather than after one.

In scope

  • A working system in your environment. Running on your data, in your tenant, behind your identity provider, connected to the systems it needs through the interfaces you already maintain.
  • The evaluation set. Real cases with the answer your own experts agree on, so you can tell a model that has drifted from one that is fine.
  • Monitoring and a runbook. What to check, the thresholds, what to do when one is crossed, short enough that your own second line reads it.
  • The code, models and prompts. In your repository, under your licence, with the documentation. You can hand the whole thing to another supplier and have them continue.

Not in scope

  • Infrastructure, service desk and systems management. We are an AI partner, not an ICT partner. Your existing supplier or internal team keeps the estate, the workplace and the helpdesk, and we build inside their standards.
  • Third-party licences and usage costs. Model and API usage, and any SaaS the solution calls, are billed to you directly so you see the real running cost. We set a ceiling and an alert rather than an estimate.
What you can verify
One real engagement

AI Predictive Maintenance for Industrial Plants

What we found. Run-to-failure maintenance meant recurring, unplanned stoppages that cost output and forced expensive emergency repairs. The producer needed early warning of failures so work could be scheduled into planned windows instead of triggered by a breakdown.

<2%Unplanned downtime (down from recurring)
−25%On-site maintenance time

Real result from a Crux Digits engagement; client name withheld pending permission.

Read the full case study →

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