Approach
From assessment to operation
Most data and AI programmes fail between the slide deck and the server. We cover both ends and the gap between them.
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01
Assessment
1–3 weeksWe start with your business goals, your data landscape and the decisions that are not working. Output: a decision map and a prioritised list of what to fix first.
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02
Tailored design
1–2 weeksNo two companies have the same systems or constraints. We design the solution around your infrastructure, your team and your budget, and sequence it into a roadmap with owners.
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03
Agile build
weekly releasesPlatform, pipelines, reports and agents are built in the open with your team and released every week. Feedback changes the next release, not the next quarter.
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04
Standards and security
every releaseAccess control, data-quality checks, monitoring, cost alerts and documentation are part of the definition of done, aligned with GDPR and good engineering practice.
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05
Continuous optimisation
ongoingWe measure adoption and impact, tune what is used, retire what is not and extend what pays back. Systems stay fast, correct and cheap to run.
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06
Knowledge transfer
throughoutTraining, runbooks and documentation accompany every delivery, so your team can run the systems with us, or without us.
Principles we do not negotiate
Real data only
No mocked numbers, no placeholder dashboards. If a value is unknown it says so.
Production-grade from day one
Monitoring, data health checks, access control and cost alerts ship with the first version, not after.
Signals over dashboards
A system that tells you when a number moves beats a dashboard someone has to remember to open.
Human in the loop
Every agent has an owner, an evaluation and an off switch. Automation earns autonomy, it does not start with it.
One owner per KPI
Metrics have definitions, owners and a single source. Otherwise two teams argue about whose number is right.
Cost you can see
Cloud spend is designed, budgeted and alerted. Our own products run for a few euros a month.