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Yanis Ghidouche · Fractional CTO

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Applied AI

Intervention report

AI creates value when its role, context and limits are explicit.

RAG, LLMs, MCP and assisted automation: I build bounded, supervised use cases tied to a concrete measure of productivity or quality.

Let’s discuss your situationDiscover the Diagnosis

Frame useful, secure and measurable AI use cases.

Chapter 1 · Inspection

What I look at before integrating AI.

  1. The specific task the team wants to speed up, make more reliable or extend.

  2. Knowledge sources, access rights, quality and freshness.

  3. The context given to the LLM, tools exposed through MCP and the limits of each action.

  4. Human review steps, the record of actions and how results are measured.

What you can expect

  • An understandable, focused AI use case integrated into the existing product.
  • A RAG and LLM loop that enriches knowledge without losing its provenance.
  • Measurable productivity or quality gains, with a human retaining the decision.

Chapter 2 · Findings

What holds AI back in a company.

  1. AI experiments multiply without a shared business problem or value measure.

  2. A generic assistant loses context, produces variable answers and weakens trust.

  3. Useful knowledge is scattered, poorly structured or difficult to keep current.

  4. Security, confidentiality and human-validation risks are addressed too late.

Chapter 3 · Intervention

How I integrate AI with human validation.

  1. Bound the use case

    I start from a real task, define the useful context and set the situations where the system must stop or request validation.

  2. Design the knowledge loop

    I connect RAG, LLMs and business sources to produce better-grounded answers and recommendations that improve over time.

  3. Secure and measure

    I limit access, log actions, maintain human oversight and measure value before expanding the scope.

Chapter 4 · Case files

In practice.

Chapter 5 · Path

From understanding to action.

  1. Step 1

    Frame

    Choose one problem, one audience, authorized data and an expected outcome.

  2. Step 2

    Prototype

    Test RAG, the LLM or MCP within a bounded scope and against explicit success criteria.

  3. Step 3

    Supervise

    Require review for sensitive outputs, track errors and keep an identifiable human decision-maker.

  4. Step 4

    Measure

    Compare quality, time saved and residual risk before generalizing the system.

Chapter 6 · Answers

AI in the enterprise: your questions.

  • Do existing tools need to be replaced to integrate AI?

    No. A focused use case can often fit into the existing product and workflows. The choice depends on the expected benefit, the data and the level of risk.

  • How do you keep control over an AI agent?

    By bounding its context and tools, limiting permissions, logging actions and requiring human validation at sensitive steps.

  • Do you have concrete examples of AI in a company setting?

    Yes. On IpedisConnect, an MCP lets an AI agent operate the platform within a controlled scope, the RAG and LLM pairing enriches the knowledge base, and an AI-assisted review flags ambiguities before human validation. On PDF Accessible, a hybrid approach brought PDF compliance to 85%, where market solutions stayed at 30%.

  • Is an LLM always the right answer?

    No. On PDF Accessible, deterministic algorithms handle structured content, symbolic AI handles semi-structured layouts, and LLM Vision only the complex layouts. Knowing when not to use AI is part of scoping.

Chapter 7 · Warning signs

Signs it is time.

  • The team discusses models before naming the problem to solve.

  • The system receives more data or permissions than its task requires.

  • No human is clearly responsible for validating a sensitive output.

  • Nobody has compared results before and after adding AI.

Next

Does this sound familiar?

A tool to prepare the conversation, a diagnosis to understand the context, and support to take action.

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