

Speakers
Abdoulaye Balde & Jacques-Alexis Verrecchia
Devoteam · Beyond Aero
About

Abdoulaye Balde
AI Engineer · Devoteam
AI Engineer (EPITA, 2025) — specialized in Computer Vision, NLP, and Generative AI. Experienced in designing and deploying end-to-end AI solutions, optimized for GPU (CUDA) and cloud (AWS, Azure) environments. Recent focus — Agentic AI & RAG on AWS: Currently building production-grade agentic systems on AWS Bedrock

Jacques-Alexis Verrecchia
Head of Product · Beyond Aero
Jacques-Alexis Verrecchia leads the development of Beyond Aero's electric and hydrogen-powered aircraft, bridging technical innovation with market viability. He also leads Beyond Aero's internal AI development, building agentic and generative tools that compress the engineering decision cycles behind a clean-sheet aircraft - putting the company at the leading edge of applying AI to aerospace development itself. His career began at Airbus, where he specialised in aeroacoustics and propulsion innovations. He later co-founded NIXYS, a medical tech startup, and served as Head of AI and DevOps at TheGreenData, focusing on predictive models for sustainability. With dual master's degrees from École Polytechnique and École des Ponts, Jacques-Alexis combines deep technical expertise with a passion for transformative solutions in aviation.
Session
From Seven Silos to One Question: MCP in Production for Hydrogen Aircraft Design
TalkDesigning a hydrogen-electric aircraft means answering questions that cross every tool boundary an engineering team owns. "Can we fly Paris to Geneva, and what does that do to our requirement baseline?" touches a 7.6 GB PostgreSQL database of real flight history, a PyTorch mission simulator, and a requirements SaaS — three systems that share no protocol, no auth model, and no vocabulary. Today the engineer opens all three by hand, and MATLAB models and the PLM are next in line. We built the layer that lets one agent answer instead, and it has been in production at Beyond Aero since July. The unit of composition is the Model Context Protocol server: one use case = one MCP server = N namespaced tool groups, published to a registry, executed through a single gateway, and switched on per user from a catalogue page — with no redeploy. This talk is the architecture and the scars, in equal measure. How a registry (what exists) and a gateway (how you call it) stay consistent when your cloud provider links neither — the namespace is the only rope, and nothing enforces it. Why a cross-region tool target is rejected outright. Why a single 64-character tool name fails the entire model call rather than just that tool. Why tools/list is paginated and what breaks when you ignore that. And what to do when a vendor's own MCP server refuses the handshake over one unimplemented capability method: we ended up authoring theirs, generating 44 read-only tools from their OpenAPI and cutting schema cost from 19.6k to 5.3k tokens. You will leave with a composition model for heterogeneous engineering tooling, the failure modes we hit in production with dates and measurements attached, and the three things we would wire differently.
Speaking at
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Cloud Native AI Summit — Paris
December 2–3, 2026