--- title: "Expertise" description: "My five areas of expertise, from AI adoption to architecture: each starts from a concrete symptom and ends with a system in use." url: "https://mc-stud.io/en/expertise" lang: "en" updated: "2026-10-12" --- Expertise # From AI adoption to the foundations that carry it Five skills, ordered by what organisations ask me for first. The first two make up the AI Adoption Lead role; the next three are what make it possible. The role I offer AI Adoption Lead Taking AI from isolated trials to measured use: choosing use cases, setting data and access boundaries, defining how results are evaluated, equipping and training teams, then tracking adoption over time. - Measurable pilots, with acceptance criteria - Guardrails: data, access, traceability - Training and a network of internal champions A role I offer on engagements, backed by the projects in my case studies. 1. [01 Operational AI](https://mc-stud.io/en/expertise#ai) 2. [02 AI adoption and training](https://mc-stud.io/en/expertise#adoption) 3. [03 Data and automation](https://mc-stud.io/en/expertise#data) 4. [04 Custom business applications](https://mc-stud.io/en/expertise#applications) 5. [05 Architecture, environments and hosting](https://mc-stud.io/en/expertise#architecture) Flagship role ## Operational AI LLMs, agents, RAG, OCR and vision models, open-weight or self-hosted models: used where they bring measurable value, with clear data boundaries and human validation. The symptom When AI was tested without a criterion or a frame, and never left the pilot. What I deliver - Use cases chosen against a written success criterion - Comparative benchmarks before any model or vendor choice - Bounded assistants and agents, with human validation - Private or local hosting when the data requires it Typical tools - LLM - Agents - RAG - OCR and VLM - Open-weight models - Evaluation Related proof 1. Healthcare, medical-device distribution 2024-2026 Through a partner AI ### [Automating after-sales returns to manufacturers, with a trace of every request](https://mc-stud.io/en/work/robotform-after-sales-forms) Santécie’s after-sales return files are routed to the right manufacturer forms and filled automatically, with an audit trail for every request. 2. IT services 2026 Direct AI ### [Training developers in agentic engineering, in eight sessions](https://mc-stud.io/en/work/agentic-engineering-training) I am preparing an eight-session path, designed to take developers from occasional use of an assistant to verifiable agentic engineering, including knowing when to do without it; first session on 16 October 2026. [See all 6 case studies in the work section](https://mc-stud.io/en/work) Flagship role ## AI adoption and training Taking AI from isolated trials to a team practice: usage rules, guardrails, agentic engineering training for developers, internal champions and usage measurement. The symptom When everyone tinkers with their own assistant, with no shared rule or measurement. What I deliver - Usage frame: allowed data, access, traceability - Agentic engineering programme for developers, on your repositories - Team primitives: AGENTS.md, skills, verified workflows - Adoption indicators defined before rollout Typical tools - Workshops - Hands-on labs - AGENTS.md - Skills - Usage measurement Related proof 1. IT services 2026 Direct AI ### [A controlled development environment, ready for coding with AI](https://mc-stud.io/en/work/coder-developer-environments) Silico’s developers have standardised environments, described as code and hosted on controlled infrastructure, the foundation of a governed use of AI. [See all 2 case studies in the work section](https://mc-stud.io/en/work) ## Data and automation Connect scattered data, automate what should be automated, and keep a trace of every decision the system makes. The symptom When the information exists but never reaches the right place. What I deliver - Map of flows and sources - Routing, processing queues and automations - Run traces and replay without duplicates - Impact indicators defined before they are measured Typical tools - SQL - [Python](https://www.python.org/) - [PostgreSQL](https://www.postgresql.org/) - BullMQ - Windmill - APIs Related proof 1. Healthcare, medical-device distribution 2024-2026 Through a partner AI ### [Automating after-sales returns to manufacturers, with a trace of every request](https://mc-stud.io/en/work/robotform-after-sales-forms) Santécie’s after-sales return files are routed to the right manufacturer forms and filled automatically, with an audit trail for every request. ## Custom business applications Internal tools shaped around your real processes: case handling, forms and approvals, back offices, mobile apps, admin portals. The symptom When spreadsheets, email and re-keying stand in for a system. What I deliver - Process scoping and MVP definition - Journeys designed with field teams - Full-stack development, web and mobile - Production release, documentation and handover Typical tools - [TypeScript](https://www.typescriptlang.org/) - Vue.js - [NestJS](https://nestjs.com/) - [Prisma](https://www.prisma.io/) - React Native - [PostgreSQL](https://www.postgresql.org/) Related proof 1. Sport and coaching 2025-2026 Through a partner AI ### [Turning a training method into a mobile app and its back office](https://mc-stud.io/en/work/nfc-training-mobile-app) The NFC method exists as a product: a mobile app for members and a back office for the team, on a single data foundation. 2. Social protection, paid-leave fund 2024-2025 Through a partner ### [A paid-leave request application for CICP2R](https://mc-stud.io/en/work/cicp-leave-requests) On the employer side, paid-leave requests and days to pay are entered in a dedicated application, then go through an explicit validation. [See all 4 case studies in the work section](https://mc-stud.io/en/work) ## Architecture, environments and hosting Foundations that last: architecture, reproducible developer environments, deployment, maintenance and documentation, with security considered from the start. The symptom When technical debt and hosting slow down every change. What I deliver - Architecture audit and recorded decisions - Developer environments described as code - Integration with your existing information system - Deployment, monitoring and maintenance Typical tools - [Docker](https://www.docker.com/) - Terraform - [Coder](https://coder.com/) - [Railway](https://railway.com/) - [Cloudflare](https://www.cloudflare.com/) - CI/CD Related proof 1. AI agent tooling 2026 Internal project AI ### [Zedflow, agent flows you can read, compose and control](https://mc-stud.io/en/work/zedflow-agent-flows) With Zedflow, an agent flow is read, validated and composed like a program, and sensitive decisions wait for a human approval. 2. Healthcare, supplementary health insurance 2026 Through a partner AI ### [Choosing a health-card reading engine on evidence, not on a demo](https://mc-stud.io/en/work/scie-ocr-health-insurance-cards) The choice of a reading engine for supplementary health insurance cards rests on a reproducible benchmark, not on a sales demo. [See all 5 case studies in the work section](https://mc-stud.io/en/work) ## Let’s talk about what AI could change for you A 30-minute discovery call, by video and with no commitment, to talk about your project, your constraints and the best next step. [Book a call](https://calendar.app.google/VQTPrCgNvQSNwdA7A) [contact@mc-stud.io](mailto:contact@mc-stud.io?subject=First%20call)