Describe your situation FR

Service family

Architecture and integration

An AI solution is only as good as its integration. We design the architecture, connect AI to your systems and put it into production reliably.

AI connected to your systems through an integration layer The AI assistant or agent does not connect directly to each application. It goes through an integration layer (APIs and connectors, the MCP protocol, least-privilege permissions, a log) that links it to the ERP, the CRM, Microsoft 365 and your databases. Assistant orAI agent IntegrationlayerAPIs and connectorsMCPlimited permissionslog ERPoperations, inventory CRMcustomers, sales Microsoft 365email, documents Databasesyour applications
A single checkpoint between AI and your software: limited access, and a record of every action.

Generative AI solution architecture

Who it's for

IT teams that must choose between several approaches (document assistant, agent, local or cloud model) and justify their choice.

Typical problem

“Three vendors are proposing three different solutions. We can't afford to get the architecture wrong.”

What we do

  • Analysis of needs and constraints: data, security, volumes, operations.
  • Comparison of options: RAG, agents, local or hosted models, cloud services.
  • Target architecture, with documented trade-offs.
  • Proof of concept on your data when the choice needs to be validated.

Deliverables

  • Architecture document: diagrams, components, data flows.
  • Architecture decision log.
  • Criteria for evaluating answer quality.
  • Proof of concept, if applicable.

Proof

L1 — Measured sovereign RAG and L4 — The same RAG on Azure: the same solution built twice to compare the options against written criteria.

AI integration with existing systems

Who it's for

Organizations that don't want one more tool, but AI inside the software they already use.

Typical problem

“Our staff spend their day juggling email, our management software and Excel files. Add one more tool and nobody will use it.”

What we do

  • Mapping of systems and integration points: APIs, databases, files.
  • Connecting AI to your tools, with access limited to what is strictly necessary.
  • Use of open protocols such as MCP (Model Context Protocol) where relevant.
  • Testing with your teams before go-live.

Deliverables

  • Working integration in your systems.
  • Technical documentation and user guide.
  • Matrix of the access granted to the AI.

Proof

L3 — Agent connected to office tools via MCP, and the project appointment reminders connected to a practice's existing software.

Production deployment and LLMOps

Who it's for

Teams that have a working proof of concept and now need to run it reliably.

Typical problem

“The demo worked well. In production, the answers change, costs go up, and nobody knows why.”

What we do

  • Reproducible deployment pipeline: versioned prompts, models and data.
  • Evaluation sets run on every change.
  • Monitoring of quality, latency, operating costs and errors.
  • Operating and rollback procedures.

Deliverables

  • Documented deployment pipeline.
  • Monitoring dashboard.
  • Evaluation set and acceptance thresholds.
  • Operations guide.

Proof

L5 — LLMOps pipeline: deployment, automated evaluation and monitoring of an AI application, documented end to end.

Other service families

Let's talk about your situation

Every organization has its own systems, data and constraints. We always start by understanding your situation. Scope and investment are then proposed in writing.

Describe your situationSee our approach