Four phases. Every one ends in something you own.

Discovery is one week in which we sit with the people doing the work, walk a single real workflow end to end, and establish where the data lives and who is allowed to see it. We interview operators, review system and data access with your IT, and score the candidate use cases by impact against integration effort. Most of the risk in an AI project surfaces here or gets paid for later, so the week is deliberately spent on the awkward questions: which system stays the system of record, what happens on the exceptions, who signs off. You end up with a scope, written success criteria and an integration plan, plus an EU AI Act risk classification. Discovery is packaged as the AI Readiness Audit, and its fee is credited against a pilot or production build signed within ninety days. The case studies show where those plans landed.

The prototype phase builds a working slice of the real system on your own data in a sandbox, over two to three weeks, wired into one of the systems the workflow already touches. It is not a demo on synthetic examples. In parallel we build the evaluation set that decides whether the result is good enough to ship, and we freeze the acceptance threshold at kickoff so nobody renegotiates the bar after seeing the numbers. You end up with a running prototype and an evaluation set carrying a baseline score, which is what a go/no-go decision needs. As an engagement this is the Pilot Sprint, fixed at €15,000, and it ends with a priced path to production if the threshold is met. The tender pipeline was measured this way per extracted field against a labelled set of past tenders.

Production means the integration goes into your live systems: access handled the way your security team requires, rollout first to a pilot group and then to everyone who needs it. The build phase runs four to six weeks inside a fixed-scope engagement of six to eight weeks from discovery to rollout, covering one workflow into one system. Monitoring, cost controls and the rollback path ship with the feature instead of arriving after it, and the security, data-protection and AI Act documentation is prepared with your own people, not handed to them at the end. You end up with the integration live for real users, running in your accounts and your repositories. As an engagement this is Production Integration, from €30,000 fixed scope, typically €30–60k. Every write-up in our case studies describes a system that reached this point.

After go-live the system is run either by your own team or by us, and the operations phase is where that choice gets made in practice. Models change, your processes change and quality drifts, so the work is continuous: evaluations kept running in CI, drift and hallucination checks, spend and latency watched, model and prompt updates shipped as measured releases instead of quiet swaps. You get a monthly report on quality, latency and spend, quarterly reviews, and a roadmap for the next workflow. If your team takes over, they inherit the documentation, the evaluation sets, the usage policy and an incident playbook, plus hands-on training for the operators and the developers who own the code afterwards. Running it for you is the Operate engagement, from €2,900 per month per system, on a three-month minimum. Either path is a real option; tell us which one you expect on the contact page.