New Services, AI & Cyber
New Services, AI & Cyber
From idea to production. We build useful digital services and integrate AI under architectural control. Innovate without losing control.
Our conviction
Why new services so often fail to deliver on their promises
Without rigorous scoping, projects drift
Without structured Go/No-Go checkpoints, projects drag on without delivering measurable value.
Compliance arrives too late
AI Act, GDPR, NIS2: caught up on urgently, they block production releases.
Underestimated integration
Some integrations first seem secondary, then account for most of the overruns.
Our engagement trajectory
Scoping
Real value, risks, regulatory constraints
Design
User journeys, architecture, technical choices
Build
Development, testing, CI/CD, AI integration
Go-live
Controlled deployment, team support
MCC
Controlled operations over time
Note: At the end of the scoping phase, we may recommend a No-Go if the value or confidence is not there.
From idea to production service
A useful, robust and scalable service: AI as a lever, trust as the foundation.
- PoC with no future Convincing demo, but no path to real production.
- Groundless development Uncontrolled stack, no product vision, debt from sprint one.
- Experimental AI Model without governance, undetected hallucinations, zero auditability.
- Security as an afterthought Added at the end as a patch, never integrated into the architecture.
- Useful service in production From idea to industrialization, with a clear trajectory and milestones.
- Mastered architecture Stack chosen to last, tested, documented, operable by your team.
- AI as a lever LLMOps, agents, automation: AI accelerates without creating hidden debt.
- Cyber integrated from design Security and compliance treated as architectural constraints, not patches.
Controlled AI
Integrating AI without losing control
Sovereign RAG architecture
Your data never feeds public model training. Complete isolation, infrastructure under your control.
Guardrails
Filters preventing the model from leaking confidential data or drifting. Bounded, auditable behavior.
LLMOps
Model governance, drift monitoring, update management over time. The model stays under control in production.
Compliance by design
AI Act, GDPR, NIS2 treated as architecture constraints from day one.
What you gain
Shorter time-to-value
Prioritized on what delivers real value. We don't build what won't be used.
Controlled risk
Security and compliance built in from the start, never added after the fact.
Maintainability
Readable, maintainable, interoperable architecture. Your team can take it over independently.
RUN anticipated
Operational handover prepared from the build phase. No "so who maintains this now?"
Frequently asked questions
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By adopting a sovereign RAG architecture (your data never trains public models), guardrails that bound model behaviour, and an LLMOps framework that monitors drift in production.
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RAG (Retrieval-Augmented Generation) connects an LLM to your document base without exposing your data. 'Sovereign' means the infrastructure remains under your control: no calls to third-party APIs, complete isolation.
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By treating them as architecture constraints from the scoping phase: AI risk classification, data minimisation, automated decision logging, access controls,not as a checklist added before go-live.
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Three recurring causes: no clear Go/No-Go milestones, regulatory compliance addressed at the end of the project, and underestimated interfaces with existing systems. Our 5-phase trajectory addresses each of these.
How do you integrate AI into a critical system without losing control?
What is a sovereign RAG architecture?
How do you ensure AI Act, GDPR and NIS2 compliance from day one?
Why do new service projects so often exceed budget?
A project to scope?
A well-argued No-Go today is better than an endless project tomorrow. We can discuss it in 30 minutes.
Talk about my project with no commitment