01
Decision-grade scoping
Expected value, assumptions, Go or No-Go criteria, main risks and initial scope clearly stated.
New Services · AI · Cyber
Designing new services operable in architectures in production.
From scoping to go-live, we integrate application services and supervised AI components into the existing system. Guardrails, traceability, human oversight and a passage to operation prepared at design stage.
Symptoms
When new services weaken the existing system.
PoCs never make it to production
Without Go/No-Go milestones, without real value measurement, the prototype drags on indefinitely without ever becoming operational.
AI is plugged in before being governed
Without architecture, without guardrails, without production monitoring: the prototype quickly becomes additional debt for the teams.
Compliance is addressed too late
AI Act, GDPR, NIS2: caught up on urgently at the end of the project, they block production releases and force redesigns.
Operations have never been thought through
Underestimated interfaces, no runbook, undesignated owners: the service reaches production without anyone having prepared its life beyond.
BEFORE Common situation
- 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.
AFTER With REELIANT
- 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.
What we deliver
What must be clear before and during execution.
02
Architecture and controls
Technical choices, IS integrations, observability, security, logging and compliance constraints made explicit.
03
First operable increment
A delivered, tested scope that can actually go live, not just a prototype or an isolated demo.
04
Operations handover plan
Runbook, owners, alerting, maintenance and MCC scope. The transition to operations is prepared before go-live.
AI under control
Integrating AI without losing control.
AI is not an add-on. It is an architectural decision : where the data flows, how the model is bounded, who monitors drift, how updates are governed. Four technical invariants follow.
Sovereign RAG architecture
Your data never feeds public model training. Complete isolation, infrastructure under your control.
Guardrails
Filters preventing the model from revealing confidential data or drifting. Bounded and auditable behaviour.
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 the design stage.
General framework: Controlled AI, our doctrine for engineering AI systems in real-world environments.
Proof in production
Useful platforms, properly integrated, sustained over time.
Build subjects are only worth their integration into the existing IS and their operability after go-live.
Energy & Professional Networks
New Extranet and consumer matchmaking platform - Les Professionnels du Gaz
Full information system for the habitA+ association, which has run the PG (Professionnels du Gaz) certification programme for 15 years. Business tools for the labelling and monitoring of 10,600 certified companies, embedded e-learning, and a real-time matching platform between professionals and consumers. Programme members: GRDF, ENGIE, EDF, Butagaz, Antargaz and biomethane stakeholders.
- 10,600 certified companies
- business tools + e-learning
- real-time lead distribution
Research, innovation & tech transfer
SEVille PUI steering platform - Erganeo
SEVille PUI steering platform for Erganeo (Paris-region SATT involved in the France 2030 programme) and its five founding institutions. Multi-institution data consolidation, ANR campaign reliability and shared hub indicators, in a sovereign environment federated on each institution's existing identity.
- Erganeo + 5 founding institutions
- 15,000 rows per campaign
- programme launched in 2024
Frequently asked questions.
How do you integrate AI into an architecture in production without losing control?
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.
What is a sovereign RAG architecture?
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.
How do you ensure AI Act, GDPR and NIS2 compliance from day one?
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.
Why do new service projects so often exceed budget?
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.
What do you deliver at the end of a digital service or AI scoping phase?
A useful scoping phase must support a launch, an adjustment or a stop decision. We deliver operable objectives, the target architecture, compliance constraints, the scope of the first increment and the go-live plan.
A project to scope?.
An argued No-Go beats an endless project. A 30-minute conversation is enough to know.
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