🧪 Experiment

Real tests, verifiable results.

The Lab gathers my experiments on AI agents, automation and business tools. Distinct from the blog: these are tests, not pedagogical content.

Recruitment·May 2026

Goal: Understand what an AI assistant can realistically bring to CV pre-screening, and where the decision must stay human.
Tools: Assistants IA généralistesn8n
Result: Exploration track, no documented test yet. Known weak spots: atypical profiles, non-linear career paths and criteria rewording.
Recommendation: Before tooling this up, clarify the criteria grid and the decision process. AI may suggest a reading order — never decide alone.

n8n·Apr 2026

Goal: Identify which sales follow-up steps benefit from automation, and which lose quality when automated.
Tools: n8nCRM
Result: Exploration track. The upstream friction is almost always the same: incomplete or duplicated CRM contact data, before any automation question.
Recommendation: Structure the CRM before automating follow-ups, and keep a human review on personalised messages.

Recruitment·Mar 2026

Goal: Assess the feasibility of an internal assistant answering new joiners' recurring questions.
Tools: Assistant IA personnaliséBase documentaire interne
Result: Exploration track. The hard part is not the assistant but the docs base: without written, current procedures there is nothing to query.
Recommendation: Start by writing and centralising procedures. The assistant only makes sense afterwards, on a low-risk HR scope.

Make·Feb 2026

Goal: Clarify how automation platform cost builds up depending on the billing model (per operation, per execution, per licence).
Tools: MakeZapiern8n
Result: Analysis note from public pricing pages, without a costed simulation: cost mainly depends on processed volume and steps per scenario — both company-specific.
Recommendation: Estimate your real monthly operation volume first. Comparing prices before that is meaningless.

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