Aligned autonomy for the physical world.
Keplen builds the physical-AI stack: signal-latent world models, governed runtime loops, and alignment infrastructure for agents that perceive, coordinate, and act.
Partners
A focused network for open physical-agent infrastructure.
Keplen works with teams across alignment infrastructure, governed runtime, enterprise agent tooling, and open AI systems.
We are inviting robotics labs, runtime builders, dataset and evaluation teams, alignment researchers, and infrastructure partners.
Mission
Autonomy is not enough. It has to be bounded, inspectable, and aligned.
Keplen’s goal is the research and infrastructure path for physical AI systems that can self-manage, coordinate in fleets, and act through explicit runtime boundaries.
The first principle is simple: agents may propose actions, but the system must verify, trace, and align before those actions matter.
- 01
Self-management
Persistent agents need explicit state, policy validity, quotas, intervention paths, and replayable evidence.
- 02
Fleet coordination
Physical AI needs node identity, capabilities, heartbeats, work orders, placement, trace sync, state handoff, and telemetry.
- 03
Alignment process
Models, runtime traces, evaluation workflows, and human review form one loop before broader autonomy is claimed.
System
A physical AI stack with a governed center.
Keplen uses Splendor Kernel as the governed runtime base, integrates Vaisys Harmony for private AI and digital-agent tooling, and works with ValueAI on open alignment infrastructure.
Physical agents stay bounded by runtime verification, while digital agents receive tooling, evidence, and trace-linked coordination.
- Signals
- Signal-latent world model
- Policy proposal
- Constraints + verifiers
- Bounded action gateway
- State commit + trace
- Alignment review
Research
The model goal is a shared latent world for signals.
Keplen’s research direction is a signal-latent world model: a model path that binds text, code, audio, visual/video, sensor, action, and state signals into a shared representation for prediction, uncertainty, planning, and alignment.
The posture is evidence-first: no model-quality claims without registered experiments, baselines, ablations, failure cases, and clear limits.
Help build the alignment layer for physical AI.
Keplen is looking for partners in robotics, world models, runtime systems, evaluation, datasets, and open agent infrastructure.