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Professional trainingGROWTHSYSTEMES / ACADEMY

Two courses.

From structuring knowledge to controlling costs, our training gives teams the methods they need to deploy AI in production.

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Two challenges, two complementary formats.

013 days

Agentic AI & ontology

Design, prototype and industrialise a reliable, measurable and governed AI system.

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024 hours

AI FinOps

Understand the real cost of AI in production and build the right optimisation levers.

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Course 01 · Agentic AI & ontology

From concept to deployment in three days.

A structured progression to create a shared AI culture, move into practice and prepare a governed production deployment.

3 daysTechnical & non-technical profiles
Objective

Align teams on agentic AI use cases, risks and architecture, then turn a priority use case into a system ready to be industrialised.

Expected outcome

A shared vision, a tested agentic prototype and a production roadmap tailored to your context.

The progression

D01

Understand & frame

Establish the foundations, qualify opportunities and choose the right use case.

D02

Build

Put the concepts into practice and create a first agentic workflow.

D03

Industrialise

Prepare a reliable, governed architecture that can be managed at scale.

By the end of the course

  • A map of opportunities and risks
  • A prototype grounded in a business use case
  • An architecture and governance blueprint
  • A production roadmap
Course 02 · In partnership with AI Value

Master the real cost of AI in production.

Scale AI in production without the bill shock.

4 hoursParis
A training course designed withAI Value · Cloud FinOps Academy
Avoid the scenario
« €4 spent on infrastructure for every €1 in revenue generated. »
AObjectives
  1. 01

    Develop expertise in optimising AI cost management.

  2. 02

    Turn complex AI cost structures into reliable insights.

  3. 03

    Inform strategic decisions through KPIs and governance.

BExpected outcomes
  1. 01

    Master the concepts, use cases and cost drivers.

  2. 02

    Use a pragmatic framework as an optimisation lever.

  3. 03

    Learn from actionable cases: lower costs, higher ROI.

02

Detailed programme

From architecture to unit economics.

01

Understand

  • Strategic introductionchallenges, ecosystem, multimodal and conceptual foundations.
  • Tokenisation and inferenceinputs, outputs, prompts and benchmarking.
  • AI architecturetechnology stacks and tokenomics: cost per token, KV cache…
02

Manage

  • AI FinOps frameworkarchitecture, rate optimisation, usage, licences and benchmarks.
  • Unit economics and KPIsROI calculations and the associated operating model.
  • Governance patternscontext engineering, prompting, RAG, fine-tuning, linear to agentic.
03

Decide

  • Use case 1 — LLM + context vs. LLM + RAGcomparing injection and indexing costs.
  • Use case 2 — SLM vs. LLMquantitative and qualitative comparison on an IT business process.
  • Use case 3 — A/B workflowscomparing models, injection and guardrails.

200+ people trained · Field experience · Reusable framework

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Tailored training

Which course addresses your next challenge?

We adapt the format to your teams’ maturity, use cases and operational constraints.

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