AI diagnostics and audit for SMEs

What is an AI diagnostic for a business?

An AI diagnostic is a structured analysis of your business processes that identifies where artificial intelligence can produce a measurable gain: time saved, errors avoided, additional revenue. It delivers a prioritized, costed and compliant roadmap before any technical investment.

The context makes it urgent: according to Bpifrance Le Lab, 55% of French SMEs were using generative AI by the end of 2025, up from 31% a year earlier — and European markets follow the same curve. Yet most of this usage remains individual and unstructured: without a diagnostic, companies pile up subscriptions with no strategy, no data governance and no measured return on investment.

Who is this audit for — and who is it not for?

This service is designed for SMEs and mid-size companies across sectors — industry, construction, services, retail, regulated professions. I work from France with clients internationally: current engagements cover France, Dubai (UAE) and Flanders (Belgium), on-site or remote, in English or French.

It is not the right fit if you only need a developer to execute an already-fixed specification — in that case, go straight to the custom AI architecture & solutions page — nor if your primary need is upskilling your teams, which is covered by corporate AI training.

How much does an AI diagnostic cost?

The initial interview is free and without obligation. The full audit is quoted as a fixed fee, typically 2 to 5 days of work depending on company size and the number of processes analysed. For French entities, part of the cost is often fundable through public schemes (Bpifrance's Diag Data IA, France Num support, OPCO funding for the training component), which I systematically identify during the debrief.

What is the audit of existing AI solutions?

More and more companies have already invested: a customer chatbot, an "AI" add-on sold by a software vendor, an agency quote for an autonomous agent. The AI solution audit evaluates these tools or offers independently, on five axes:

• Business relevance — does the tool solve a real problem, with a measurable gain?
• Technical soundness — models used (GPT, Claude, Gemini, Mistral), architecture (RAG, agents, fine-tuning), dependencies and reversibility.
• Total cost — licences, API consumption, maintenance, hidden data-cleaning costs.
• Security and confidentiality — where your data goes, who processes it, under which contractual guarantees.
• Compliance — EU AI Act classification of the system, GDPR obligations, required documentation.

The debrief gives you a reasoned verdict: keep, renegotiate, replace or stop.

Why does an independent view change the outcome?

My position is that of an independent consultant: I resell no licences and attach no mandatory development to the audit. Recommendations can therefore conclude that simply configuring your current tools is enough — it happens regularly. With more than twenty years in digital transformation, 50+ companies advised and an expert role with France's Collège Numérique France 2030 programme, I also bring the technical depth: agent design, RAG, MCP, fine-tuning, low-code platforms — detailed on the AI architecture page.