Services

Three packaged offers to meet your data, AI and product needs.

Audit & Strategy

  • Data maturity assessment
  • Recommended roadmap
  • Quick wins identified

Technical support

  • Architecture & design
  • Pipeline implementation
  • Knowledge transfer

Delivery & Execution

  • Project delivery leadership
  • Team coaching
  • Iterative delivery

AI Architecture Framework

Operating framework to design, industrialize and govern production AI systems.

Strategy & Scoping

Executive scoping, ROI prioritization, roadmap.

Data & Infrastructure

Resilient pipelines, storage, ingestion and quality.

Model & Orchestration

Model selection, training, updates and orchestration.

Deployment & Observability

CI/CD, monitoring, alerting, retraining triggers.

Governance & Security

Access control, traceability, compliance, risk review.

Trust signals

Method, governance and delivery discipline - no marketing claims.

Delivery discipline

  • Scoping: goals, constraints, risks, acceptance criteria
  • Architecture: ADRs, reviews, standards, threat model
  • Run: observability, incidents, SLO/SLI, post-mortems

Security & governance

  • RBAC / least-privilege, audit trails
  • Data classification & retention
  • LLM safety: prompts, evals, light red teaming
  • Compliance: privacy-by-design (when applicable)

Artifacts (on request)

  • Statement of Work (SOW) / delivery plan
  • Architecture diagram + runbook
  • Risk register + DPIA checklist
  • Cost model (FinOps) & capacity assumptions

Documents and examples available on request.

Engagement models

Three formats to scope, design, and drive a production AI system.

  • Strategic AI advisory (executive scoping, prioritization, ROI)
  • AI architecture design (LLM/RAG, data platform, MLOps, security)
  • Technical leadership & oversight (architecture reviews, delivery oversight, standards)

Why me

  • Systems thinking
  • Production experience
  • Executive communication
  • Governance & security awareness
  • Cross-domain AI architecture

How I work

1

Discovery

Understanding context and objectives.

2

Design

Architecture proposal and action plan.

3

Implementation

Incremental and iterative delivery.

4

Handover

Team enablement and documentation.

Architecture d’un système IA moderne

Data Sources

Ingestion

Warehouse

ML / LLM

API

Dashboard

Ressources

Checklist Audit Data

Structure d’audit rapide pour cadrer la qualité, les flux et les dépendances data.

Détails disponibles sur demande.

Template Architecture IA

Trame de référence pour concevoir une architecture IA claire, modulaire et scalable.

Détails disponibles sur demande.

Guide LLM en entreprise

Points clés pour cadrer les usages LLM, la sécurité et la mise en production.

Détails disponibles sur demande.