Applied AI · Cognitive Robotics · Embodied Intelligence
We build the cognitive layer for systems that work across the physical and digital world — enterprise workflows, factory floors, robot fleets.
Manifesto
S0 is the initial state — the starting point of every system before its first action, its first observation, its first reward signal. Every capable machine was once nothing. We build the layer that changes that.
Intelligence has to be designed in from the start. Whether the system runs on a factory floor, a financial institution, or an enterprise workflow — cognition is what makes it work. That's what we build.
Core Capabilities
Expertise
Platforms
One cognitive architecture across physical and digital operating environments. The deployment surface changes; the intelligence layer stays the same.
Fleet Intelligence
S0 coordinates mixed fleets working toward shared objectives. Each agent type handles what it does best — humanoids for precision tasks, drones for aerial coverage, quadrupeds for rough terrain. The coordination layer keeps them working as one.
Domains
Humanoid teleoperation and sim-to-real transfer for assembly and logistics. Digital twin infrastructure for validating policies before physical deployment.
Agents that build and improve other agents. Enterprise workflow automation with LLM orchestration, self-evolving architectures, and RLHF feedback loops.
Automated document analysis and AI-assisted decision pipelines for credit operations, debt recovery, and portfolio management.
Language models trained on operational and regulatory data. RLHF-aligned assistants for grid operators, maintenance engineers, and technical support.
Benchmark design for RL and embodied agents. AI-powered tools for adaptive learning and academic workflow automation.
Aerial and ground inspection robots for bridges, power lines, and industrial facilities. Autonomous surveying with structured reporting pipelines.
Partners
How We Work
Every deployment starts by formalizing the task as a Markov decision process — states, actions, rewards, constraints.
High-fidelity simulation with domain randomization. Policies are stress-tested before a single actuator moves in the real world.
Structured sim-to-real transfer with a progressive curriculum. Continuous validation against real-world edge cases.
On-device learning keeps policies sharp in the field. The robot improves on the job, within safety bounds.
Contact
We're building the founding team and first partnerships. If you're working on serious robotics, let's talk.
Research partners · Early customers · Talented engineers welcome