I build multi-agent AI systems, spec-driven engineering workflows, and offline-first SaaS architectures designed for high-trust environments where privacy, reliability, and auditability 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 rigorous 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.
- 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 audit-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 (OpenSpec / TechDocs)
- 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.
I’m currently designing the architecture and engineering framework for Solstice, a new offline-first mobile application focused on structured personal productivity and intelligent note workflows.
The MVP explores:
- 100% local-first (no cloud, no analytics, no data egress)
- Instant interaction with on-device AI
- Privacy as a design constraint
- Minimalist UX built for cognitive clarity
- Intelligent note expansion using deterministic, local AI
- Context-aware organization of study materials and personal notes
- Distraction-free writing workflow
- Local semantic search over user data (documents, notes)
- Zero-latency offline operation in low-connectivity environments
The project is built with a privacy-first philosophy and intentionally excludes medical, diagnostic, or regulated functionality.
More will be shared publicly after the MVP is released.
- 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.