Question 1

How much does an enterprise AI project cost?

Accepted Answer

Costs range from €5,000 (maturity audit) to €50,000+ (full deployment). A first quick win typically costs between €5,000 and €15,000. ROI is measured in hours saved, errors avoided and additional revenue — often visible within the first month.

Question 2

How do you measure the ROI of an AI project?

Accepted Answer

We define KPIs before the project: time spent on the automated task, number of errors, processing time, cost per operation. We measure before and after. A good AI project should pay for itself within 3 to 6 months.

Question 3

Is AI profitable for an SMB?

Accepted Answer

Yes. AI costs have dropped tenfold in 3 years. Automating invoice processing, document writing or customer support often costs less than a part-time hire. The key is to target the right use cases.

Question 4

Does our data stay confidential?

Accepted Answer

Absolutely. We use GDPR-compliant solutions, hosted in France whenever possible. Your data is never used to train third-party models. We can work with self-hosted models when sensitivity requires it.

Question 5

Do we have to give access to all our data?

Accepted Answer

No. We always work on the principle of minimization: access only to the data needed for the targeted use case. Access rights are defined at the start of the project.

Question 6

How do you handle GDPR compliance with AI?

Accepted Answer

We apply a pragmatic checklist: minimizing the data collected, limited retention period (3 years maximum), consent where necessary, traceability of processing. We provide you with the required documentation.

Question 7

How long before we see results?

Accepted Answer

2 to 4 weeks for the first quick wins. A full maturity audit takes 10 business days. An end-to-end project (audit → deployment) lasts 2 to 4 months depending on complexity.

Question 8

Do we need an in-house technical team?

Accepted Answer

No. We handle the technical side (development, deployment, monitoring). You need a business champion who knows the processes to improve. We transfer skills gradually.

Question 9

How is a typical AI project organized?

Accepted Answer

In 4 phases: (1) diagnosis/audit, (2) prioritization of use cases, (3) development and deployment of the first use case, (4) measurement and iteration. Each phase has concrete deliverables and validation criteria.

Question 10

What is an AI agent in business?

Accepted Answer

An AI agent is a system that can understand a request, retrieve the relevant information (often from your internal documents), then act within your tools — under control. It's different from a classic chatbot: it can access your data, execute actions and cite its sources.

Question 11

What is RAG and when should you use it?

Accepted Answer

RAG (Retrieval-Augmented Generation) lets an AI answer based on your internal documents. We retrieve the relevant excerpts, generate the answer and keep the traceability. Useful for internal support, compliance, procedures — anything that needs sourced answers.

Question 12

How do you move from a POC to production?

Accepted Answer

That's the critical point. You need regression tests, monitoring (quality, costs, latency), security (prompt injection, data leaks) and continuous evaluation. This is what we call LLMOps — and it's our specialty.

Question 13

Will AI replace our employees?

Accepted Answer

No. AI replaces tasks, not people. Your teams do the high-value work — AI handles the rest. The result: more efficient, less frustrated, and often more motivated.

Question 14

How do we train our teams in AI?

Accepted Answer

We offer tailored training: from awareness sessions (2h) to intensive programs (12 weeks). The key: we train on your teams' real use cases, not on theory. The goal is autonomy.

Question 15

How do you ensure adoption by the teams?

Accepted Answer

Adoption relies on three levers: (1) showing fast results (quick wins), (2) training on the tools actually used, (3) supporting over time (coaching, monthly support). Without these three elements, the tool ends up in a drawer.

Enterprise AI FAQ

Costs & ROI

Data & confidentiality

Timelines & organization

AI agents, RAG & production

Training & adoption

How about we start by talking it through?