01
Phase 1
Specification & architecture
Defining the functional scope, choosing the technical building blocks, the target architecture. Documenting the use cases and the acceptance criteria.
Tailored Implementation
From strategy to the solution that runs.
Building and deploying operational AI solutions built to last. Automation workflows, business agents, RAG systems, connectors: concrete, documented, transferable to your team.
The observation
Plenty of demos. Few solutions that hold up in production.
A PoC is easy. A solution that integrates with your systems, that handles the load peaks, that survives the first unhappy customer ticket: that's another story.
Implementation is the stage where AI leaves the slide to meet the reality of your business. Architecture, integration, monitoring, transfer: so many dimensions you don't improvise.
A solution should serve your team, not hand them another problem to manage.
The methodology
Four phases, a solution that holds.
From the functional scope to going live, every step produces verifiable deliverables.
02
Phase 2
Iterative development
Building in testable increments. Regular demonstrations, continuous adjustments. You watch the solution take shape, you don't discover it at the end.
03
Phase 3
Integration & testing
Connecting to existing systems (CRM, ERP, business databases), integration tests, real usage scenarios. Validation by the operational teams.
04
Phase 4
Deployment & transfer
Going to production, reproducible documentation, team training. You walk away with a self-reliant solution, not a dependency.
Expertise on tap
The building blocks I master.
Four areas of technical expertise, combined to your real need, not to tick boxes.
Automation & agentic bridges
Orchestrating your tools (CRM, ERP, messaging, data providers) into robust workflows: n8n, Make, Zapier or custom solutions depending on context. When the logic exceeds what a linear workflow can handle, I connect these flows to more sophisticated agentic frameworks, without breaking what's already there.
AI agents & orchestration
Agents designed to carry out concrete tasks: lead qualification, support, document processing. For complex cases, decomposition into sub-tasks delegated to specialised agents, with parallel execution where possible. Multi-model, local-first when confidentiality requires it.
RAG & persistent memory
Queryable knowledge bases: internal documentation, contracts, procedures. Sourced answers, no black box. Contextual memory that persists from one session to the next: your agents keep the thread without relearning everything each time.
MCP connectors & traceability
Linking your business data to your AI tools via the Model Context Protocol. Standardisation, security, reusability. Every agent decision stays logged and auditable: you understand why an agent acted, not only that it acted.
Deliverables
What you receive.
Operational solutions
Workflows, AI agents, RAG systems or connectors: deployed, tested, integrated into your environment.
Technical documentation
Architecture, implementation choices, dependencies, usage scenarios. Readable by your team, not only by me.
User guides
Operating procedures for end users. No opaque technical manual: concrete material, illustrated with business examples.
Monitoring framework
Indicators of healthy operation, alerts, logging. You know at any moment whether the solution is keeping its promises.
What you gain
A solution that belongs to you.
Deployed, not demonstrated
Proofs of concept stay in the demos. Here, the expected output is a solution running in production that your team actually uses.
Controlled technical debt
Documented code, explicit dependencies, justified implementation choices. No black magic, no black box: legible engineering.
Real autonomy
By the end of the project, your team can evolve, maintain and extend it. The knowledge stays with you, not in my head.
Operational ethics
Confidentiality, traceability, data governance: these dimensions are built in from the design stage, not bolted on as an afterthought.
Frequently asked questions
What I'm asked most often.
Do I need to have run an audit or defined a strategy first?
It isn't mandatory but it's recommended. Without framing, the risk is building a solution that doesn't address the right problem. We can compress this phase if necessary.
How much customisation versus off-the-shelf tools?
Bespoke isn't dogma. When an off-the-shelf tool covers your need, I'll tell you. An in-house implementation is justified when customisation, sovereignty or deep integration require it.
How do you guarantee security and confidentiality?
The architecture is designed around the data processed: local-first deployment when needed, anonymisation, logging, GDPR and EU AI Act compliance. Security is an architectural criterion, not an option.
What happens after delivery?
Three options: full autonomy thanks to the documentation and training, occasional support when needed, or a monthly continuous-optimisation retainer. You choose the level of continuity that suits you.
Ready to move to execution?
Book a discovery call. We assess together whether tailored implementation is the right answer to your current need. No commitment.