I build validated MVPs in short cycles, focusing on audit-ready, traceable systems where spec-driven discipline and execution rigor are essential.

I run a deep-tech venture studio (currently in stealth) focused on orchestrating and scaling products that combine local-first AI, deterministic pipelines, and audit-grade specification governance. My work sits at the intersection of AI engineering, systems design, and product strategy — ensuring every deliverable is traceable, reproducible, and compliant by default.

• Regulated-domain, offline-first professional system
• Infrastructure-grade tooling for safety-critical contexts
• Early-stage system incubated within the studio
• Privacy-first, local-only information management
• EU compliance-oriented regulatory tooling
• Internal build and execution infrastructure
• Research initiative on systems and human factors
• Structured preference and decision experimentation
• Language-centric learning and signal extraction system

• Engineer spec-first multi-agent systems and orchestration pipelines
• Build local/on-device AI workflows for privacy-critical use cases
• Design offline-first architectures with deterministic behavior
• Govern full product lifecycles using technical specifications and due-diligence ready documentation
• Architect AI Act–aligned compliance frameworks
• Lead end-to-end venture incubation: research → architecture → MVP
• Mentor AI collaborators inside heterogeneous multi-model ecosystems

• Multi-Agent AI Orchestration
• Context Engineering
• Local-First / Offline-Only Architectures
• Safety-Aware & Compliance-Centric AI
• Specification Governance (reviewable, deterministic)
• EU MDR / AI Act Alignment

• Python, TypeScript, Swift, VS Code, GitHub
• Multi-agent orchestration frameworks
• Deterministic code-gen pipelines
• Local RAG systems
• Frontier models (selective, purpose-bound usage)

I focus on technologies that provide engineer-level control, audit trails, and predictable behavior — avoiding black-box dependencies whenever possible.

Specs, constraints, and traceability are the foundation of high-trust systems.

Latency, reliability, and sovereignty matter. Everything that can run on-device, should.

Tools should behave predictably. Reproducibility is a feature.

Agents require context discipline, shared memory, and unified instruction layers.

Compliance isn't overhead — it's a differentiator.

Only non-commercial, safe, and non-confidential repositories appear here. Active venture projects remain private inside the studio infrastructure.

• LinkedIn: https://linkedin.com/in/elói-ramos
• GitHub: https://github.com/EloiRamos

If you're working on AI agents, local-first applications, or spec-driven engineering, feel free to connect.