--- title: "Matisse Chenavas, AI Adoption Lead and engineer" description: "I am Matisse Chenavas, AI Adoption Lead and software engineer: I bring AI and agentic engineering into the real work of teams." url: "https://mc-stud.io/en" lang: "en" updated: "2026-10-12" --- Hi, I’m Matisse Chenavas # I take AI from the pilot to your teams' real work. - AI Adoption Lead - software engineer - founder of MC Studio With you, I pick the right use cases I help teams adopt AI without breaking what works: measurable use cases, protected data, agents that fit their methods, and people trained to use them. I have shipped software for real organisations since 2022, and I work with agents myself every day. [Talk about your project](https://mc-stud.io/en/contact) [See the work](https://mc-stud.io/en/work) ![Portrait of Matisse Chenavas.](https://mc-stud.io/_astro/01-hero-portrait.CTqtA5AW_1wswFE.jpg) Who I am ## An engineer who stays until it is used I learned the trade by shipping. Trained at 42 Lyon, I signed my first developer contract in 2022. Since then I have built systems that serve real users, mostly from inside my clients' teams: an after-sales automation in production in healthcare, a mobile app and its back office, a document-reading benchmark under health-data constraints. In January 2026 I founded MC Studio to carry this work under my own name. My field: operational AI and agentic engineering, where security, traceability and adoption matter as much as the model. I work with agents every day, framed by written rules, skills and verified workflows; this site is built that way. That is the method I bring to the teams I join, and when a project calls for it, I can bring together a small team of hand-picked independents. **First developer contract** 2022 **MC Studio, founded in January** 2026 **Trained at** 42 Lyon **Working languages** FR · EN [My background](https://mc-stud.io/en/about) Landmarks ## A few facts you can check on this site **client organisations, directly or alongside partners** 5 **published case studies, limits included** 9 **projects that integrate AI or train people to use it** 7 **tools published on GitHub for working with agents, 5 of them open source** 6 Every case study and tool counted here can be read on this site. I have shipped software since 2022, and MC Studio has existed since January 2026. Expertise ## What the market asks me for first Operational AI and its adoption by teams come first: that is where projects most often fail, and where I am most useful. The rest of the chain follows, because an agent without reliable data or an application to live in is useless. 1. Flagship role ### [Operational AI](https://mc-stud.io/en/expertise#ai) When AI was tested without a criterion or a frame, and never left the pilot. 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. 2. Flagship role ### [AI adoption and training](https://mc-stud.io/en/expertise#adoption) When everyone tinkers with their own assistant, with no shared rule or measurement. Taking AI from isolated trials to a team practice: usage rules, guardrails, agentic engineering training for developers, internal champions and usage measurement. 3. ### [Data and automation](https://mc-stud.io/en/expertise#data) When the information exists but never reaches the right place. 4. ### [Custom business applications](https://mc-stud.io/en/expertise#applications) When spreadsheets, email and re-keying stand in for a system. 5. ### [Architecture, environments and hosting](https://mc-stud.io/en/expertise#architecture) When technical debt and hosting slow down every change. [All expertise](https://mc-stud.io/en/expertise) Working inside teams ## I join your team, then I upgrade the way it works I mostly work inside my clients' teams, on projects I did not all start. It gave me a method: join your rituals first, understand what already works, then set up, sized to the team, the agentic work primitives I use every day at MC Studio. 1. ### Rules written for humans and agents An AGENTS.md, one page per project, recorded decisions. The newcomer and the agent read the same source, and it gets reviewed like code. 2. ### Know-how as skills A recurring procedure becomes a skill the agent loads at the right moment. In my own work, preparing the monthly activity report is one, with its verification rules. 3. ### Graph workflows, with human approval Long tasks become graphs of steps, with resumption and human decision points. That is what pi-graph and Zedflow are for, two tools I publish. 4. ### Proof before trust Tests, checks and reviews are part of the flow. An agent that cannot show what it verified is not done. This site is built that way: several agents with assigned files, written instructions, a build and a type check on every pass, and my review before publishing. [See my open-source tools](https://mc-stud.io/en/open-source) Work ## Proof files, not a gallery Each case study reads in one go: the key points in thirty seconds, then the story, the proof and the limits. 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. 3. 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. [All work](https://mc-stud.io/en/work) Clients and tools ## Who I work with, and with what ### Clients and partners - [Santécie](https://santecie.com/) - [Essan NFC](https://www.essannfc.com/) - [Silico](https://silico.fr/) - [Serial Coders](https://serialcoders.fr/) ### Everyday technologies - [TypeScript](https://www.typescriptlang.org/) - [React](https://react.dev/) - [NestJS](https://nestjs.com/) - [Prisma](https://www.prisma.io/) - [PostgreSQL](https://www.postgresql.org/) - [Python](https://www.python.org/) - [Rust](https://www.rust-lang.org/) - [Docker](https://www.docker.com/) - [Coder](https://coder.com/) - [Cloudflare](https://www.cloudflare.com/) - [Railway](https://railway.com/) - [Anthropic / Claude](https://www.anthropic.com/) - [LangChain](https://www.langchain.com/) - [Ollama](https://ollama.com/) - [vLLM](https://vllm.ai/) - [Hugging Face](https://huggingface.co/) - [NVIDIA](https://www.nvidia.com/) - [Astro](https://astro.build/) - [GitHub](https://github.com/) Every logo links to the official site of the organisation or project. Trademarks belong to their owners. For IT firms and agencies ## My expertise, serving your clients Your clients ask you for AI and agents, and your teams already ship a lot. I know subcontracting from the inside: your client stays yours, I bring the skills you lack, and I train your teams so they keep them. [Work together](https://mc-stud.io/en/partners) 1. I do not take your territory 2. I bring what you lack 3. I train your developers Agents and open source ## Tools for engineers who code with agents I publish the tools I build for my own work with agents: an agent-flow engine, extensions for the Pi coding agent, skills, my context configuration. Each with its install command, what it does and when to reach for it. Beta Pi context ### [pi-context-tree](https://mc-stud.io/en/open-source/pi-context-tree) An extension for the Pi coding agent that injects the right context at the right time, by path and operation, from CONTEXT.json files versioned in the repository. **12** commits / 12 months v0.4.2 `ZEDIUM-Off/pi-context-tree` Beta Pi subagents ### [pi-runtime-monitor](https://mc-stud.io/en/open-source/pi-runtime-monitor) A lightweight Pi extension that shows, in the footer, the CPU, memory and aggregated usage of your session’s subagents. **3** commits / 12 months v0.1.1 `ZEDIUM-Off/pi-runtime-monitor` Experimental skills planning ### [plan-writer](https://mc-stud.io/en/open-source/skills) My shared agent skills, installable with the skills CLI; for now plan-⁠writer, which helps write structured implementation plans. **4** commits / 12 months `ZEDIUM-Off/skills` [All tools](https://mc-stud.io/en/open-source) Articles ## Field notes What I learn by shipping and by working with agents: methods, lessons, limits. - October 11, 2026 5 min read AI ### [Joining a team, then upgrading the way it works with agents](https://mc-stud.io/en/articles/joining-a-team-and-installing-agentic-primitives) How I join an existing team and install, sized to it, agentic work primitives: written context, recorded decisions, skills, workflows with human approval, proof before trust. - October 8, 2026 5 min read AI ### [Automating a sensitive process without losing traceability](https://mc-stud.io/en/articles/automating-a-sensitive-process-without-losing-traceability) Automating a process that touches health data, payroll or invoicing is not about speed. It is about memory: knowing, afterwards, who decided what, under which rules and on which data. [All articles](https://mc-stud.io/en/articles) ## 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)