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AI agents, automation & sovereignty from Bordeaux

AI expert in Bordeaux to automate your processes with AI agents that are useful, measured and hosted where you decide

From Bordeaux, I build AI agents that actually do the work: business process automation, document RAG, connection to your tools. On Azure OpenAI and Microsoft Fabric when your stack is Microsoft, on European providers or your own infrastructure when sovereignty comes first. From SMBs to large groups, with the same level of rigour.

9 years of data & cloud experience AI agents, RAG, MCP Azure, Scaleway or self-hosted SMBs, mid-caps & large groups
use cases

AI use cases I can deploy

The goal is not another demo, but automating a real process: observable, secure, measured by cost per task and genuinely integrated with your data.

01 — documents

Document AI agent

Connect AI to your PDFs, contracts, procedures, tickets or knowledge bases with sourced answers, access rights and logs.

RAGAzure AI SearchSharePoint
02 — processes

Business process automation

Classify, extract, route and draft: emails, forms, tickets, invoices or internal requests handled end to end, with human validation on high-stakes cases.

WorkflowAI agentsSupervision
03 — analytics

Reporting / Power BI / Fabric assistant

Help business teams query their data, understand indicators and generate actionable analysis.

FabricPower BIDAX
04 — tools

Internal AI agent connected to tools

Create an assistant connected to your APIs, CRM, SharePoint, SQL databases, files or business workflows with guardrails and traces.

AI agentsAPIMCP
offers

A clear entry point to launch AI

30 min

Company AI audit

A short discussion to identify the most valuable AI use cases: automation, document assistant, AI agent, reporting, support or request qualification.

1 to 3 days

Full AI audit

Process mapping, ROI / complexity / risk scoring, target architecture, budget estimate and 30 / 60 / 90 day roadmap.

POC

AI agent / RAG

Useful prototype on your real data: sources, permissions, prompts, evaluation, logs, monitoring and production-readiness criteria.

production

Industrialization and go-live

Target architecture, security, CI/CD, supervision, cost per task, governance and GDPR compliance — on Azure and Microsoft Fabric, on a European provider or on your own infrastructure.

hosting

Where your AI agents run, and why that is a separate decision

The model and where it runs are two distinct decisions. I work at all three levels and arbitrate with you based on data sensitivity, volume and the expected level of sovereignty — not on a vendor preference.

01 — microsoft

Azure OpenAI & Microsoft Fabric

The natural choice when your information system is already Microsoft: Entra ID, Key Vault, Azure AI Search, Fabric and Power BI integration, with the compliance and support a large-group IT department expects.

Azure OpenAIFabricEntra ID
02 — europe

European providers

Scaleway, Hostinger or another European operator to run open-weight models on servers located in the EU, with a DPA, identified sub-processors and no CLOUD Act exposure.

ScalewayHostingerOpen weight
03 — your own

Self-hosting

Your own GPUs or datacenter when data must not leave. A real entry cost, full control, and quick payback above a certain volume. It is what I run for my own agents.

On-premiseGPUMCP
measurable mvp

Start with an AI MVP and a clear target

Before industrializing an AI agent or automation, I prefer a short, measurable scope linked to a real business objective. The idea is simple: quickly prove whether the use case deserves to go further.

01 — target

Define the expected outcome

State what the MVP should improve: processing time, cost per request, answer quality, automation rate or production delay.

AI MVPROIFraming
02 — measure

Track simple indicators

Set up a few readable metrics: time saved, cost per action, usage rate, sourced answers, user satisfaction and errors to fix.

KPIFinOpsQuality
03 — decision

Know whether to continue, pivot or stop

A good POC should not live forever. It should help decide: industrialize, reduce scope, change model or return to a more classic automation.

GovernanceRiskRoadmap
differentiation

An AI approach grounded in Data Cloud

My approach starts with the need, the available data and the company constraints. Only then come the model choice, the AI agent, the RAG system or the automation to implement.

The work combines AI agents, business automation, RAG, Data Cloud architecture, governance, monitoring, FinOps and team enablement. The goal is to move from an appealing POC to a tool that is actually used, measured and maintainable.

I adapt to the size of the organization: an SMB needs a short scope and a fast result, a large group needs an architecture that clears the security review, compliance, access governance and the investment committee. I have worked in both contexts, across retail, finance, healthcare, real estate, public sector and consulting environments.

I test what I recommend and publish the measurements. Two examples: what an AI agent task really costs, and my private two-agent self-hosted architecture.

Azure OpenAIMicrosoft FabricScalewayOpen weightTerraformGDPRFinOps
local

AI support around Bordeaux

From Bordeaux, I support companies of all sizes in Bordeaux, Mérignac, Pessac, Talence, Bègles, Eysines, Bruges, Le Bouscat, Gironde and across France remotely.

BordeauxMérignacPessacTalenceBèglesEysinesBrugesLe BouscatGirondeFrance remote
faq

Frequently asked questions about AI projects

How much does AI support in Bordeaux cost?

The budget depends on the level of support: framing session, AI audit, AI agent/RAG MVP or Azure industrialization. The right first step is to prioritize use cases by ROI, complexity, risk and ability to measure results.

What is the difference between an AI agent and a chatbot?

A chatbot answers questions. An AI agent can use tools, read sources, trigger actions, follow a workflow and produce usable traces.

Can AI be connected to my internal documents?

Yes, with a document RAG architecture: indexing, semantic search, sourced answers, access rights management, logs and supervision.

Do you work on Azure OpenAI or on sovereign solutions?

Both, deliberately. Azure OpenAI, Microsoft Fabric, Azure AI Search, Entra ID and Key Vault fit naturally when your information system is already Microsoft, and that is often the right call in a large group. When sovereignty or cost comes first, I deploy open-weight models on a European provider such as Scaleway or Hostinger, or directly on your infrastructure. The model and where it runs are two separate decisions.

Can AI run without sending data outside Europe?

Yes. Open-weight models run on GPUs operated inside the European Union — Scaleway and Hostinger both offer this — or on your own infrastructure, with a DPA, identified sub-processors and no CLOUD Act exposure. The price is paid in infrastructure work rather than an API key. It is a trade-off to state explicitly, with the DPO as much as with the engineering team.

Which business processes can an AI agent automate?

In practice: handling inbound requests, extracting and normalising data, classifying and routing tickets, reading documents or logs, drafting replies and follow-ups. The rule I apply is to route by cost of error: a routine step goes to a cheap model, a high-stakes decision stays on a frontier model or with a human.

Do you work with large groups or only with SMBs?

Both. An SMB expects a short scope and a fast result; a large group expects an architecture that clears the security review, compliance, access governance and the investment committee. I adapt the method and deliverables to the context, with 9 years of experience in Data, Cloud and BI across retail, finance, healthcare, real estate, public sector and consulting environments.

Do you work from Bordeaux, Gironde and remotely?

Yes. From Bordeaux, I support companies of all sizes in Bordeaux, Mérignac, Pessac, Talence, Bègles, Eysines, Bruges, Le Bouscat, Gironde and across France remotely.

Want to identify the right AI use cases for your company?

We can start simply: 30 minutes to understand your context, data, constraints and AI use cases that are really worth the effort, with a first view of measurable objectives.

Book an AI audit