Constraint is the research question
Frontier labs optimize capability at scale. Few groups own the question of verified deployment under constraint.
HOPN Lab 路 research agenda
How do we build AI systems that organizations can trust, audit and afford to run themselves?
HOPN Lab studies AI systems that stay reliable, private and verifiable under real-world constraints: limited compute, no cloud access, sensitive data, regulated domains and physical safety. Our work is organized as three layers (Route, Ground, Assure), one foundation program and one physical-world track.
This page describes our research agenda. Status labels show what is planned, in progress or complete.
Frontier labs optimize capability at scale. Few groups own the question of verified deployment under constraint.
GDPR, the EU AI Act and data sovereignty make constraint a requirement, not a weakness.
We publish our protocols and raw logs, and we disclose our commercial relationships.
Five short pictures. No scores. Click a step, then try the live Graph RAG demo.
| Program | Question | Direction |
|---|---|---|
| Route | How do we compose many models and providers behind one reliable interface? | Policy-driven routing across cost, latency, privacy and quality |
| Ground | What does the system know, and how do we prove where it came from? | Provenance-first knowledge graphs |
| Assure | How do we know each output is good enough, at runtime, before it ships? | Quality orchestration with contracts and service levels |
| Foundation | What quality per watt and per euro is possible when data never leaves the organization? | Private and efficient AI |
| Physical | How do Route, Ground and Assure apply where errors have physical consequences? | Verified physical autonomy |
Each page is one question, the planned work, and the metrics we will report later.
How do we compose many models and providers behind one reliable interface?
What does the system know, and how do we prove where it came from?
How do we know each output is good enough, at runtime, before it ships?
What quality per watt and per euro is possible when data never leaves the organization?
How do Route, Ground and Assure apply where errors have physical consequences?
Public project names only. Physical-track hardware names stay private until positioning is set.
| Project | Route | Ground | Assure | Foundation | Physical | Role |
|---|---|---|---|---|---|---|
| AI-Pass | Yes | No | No | No | No | Infrastructure layer for the other tracks |
| TEAOA | Yes | No | No | No | No | Trust model for orchestration |
| SLDE-AFT and offline LoRA extraction | No | Yes | No | Yes | No | Corroborated extraction feeding the knowledge graph |
| Semvec | No | Yes | No | No | No | Temporal and versioned memory |
| ServAlign | No | No | Yes | No | No | Constraints and policy as machine-checkable objects |
| Invoice automation | Yes | Yes | Yes | No | No | Shared testbed |
| Sovra | No | Yes | No | Yes | No | Compute-spectrum efficiency, private RAG, graph pruning |
| Item | Program | Months | Status |
|---|---|---|---|
| AI-Pass submission | route | 0 to 2 | In progress |
| Quality orchestration charter and shared invoice benchmark | assure | 0 to 3 | Planned |
| Simulation baseline with fault injection (ROS 2, Gazebo) | physical | 0 to 3 | Planned |
| Graph-pruning study | ground | 3 to 6 | Planned |
| First quality-routing results | assure | 3 to 6 | Planned |
Buyers, students and funders: write a short note. We reply from contact@ehopn.com. Or open the live demo first.
Questions? Email contact@ehopn.com