THINK.DECIDE.ACT.
The decision-making brain for autonomous agents. combining vision, language, and state into intelligent, sovereign action.
HOW LAM WORKS
Learning the World
LAM loads specialized AI modules: Vision (CLIP), Language (LLaMA), and State sensors to form complete situational understanding.
Training by Doing
Reinforcement Learning (RL) allows agents to learn from trial and reward, acquiring behaviors rather than just facts.
Optimizing Decisions
Proximal Policy Optimization (PPO) ensures safe exploration, preventing unstable actions in critical environments.
Federated Mode
Devices train locally. Only compact gradient updates are sent to the central server, preserving privacy.
Smart Aggregation
Trajectory weighting prioritizes updates from devices with more completed missions over raw data quantity.
Global Improvement
The global model improves and redistributes updated policies to all fleet devices, creating a smarter swarm.
FEDERATED
COLLECTIVE INTELLIGENCE
Two factories, one brain. Factory A learns to handle varying light. Factory B learns new material handling. Through federated averaging, they share skills without ever sharing raw video feeds or proprietary production data.
Factory A (Germany)
Learning: Low-light inspection
Factory B (Poland)
Learning: Reflective surfaces
CORE CAPABILITIES
Everything you need to deploy intelligent, automated decision systems at scale.
Multi-Modal
Combines vision, language, and sensors.
RL (PPO)
Learns from trial and reward safely.
Federated
Distributed learning, local privacy.
Experience Weighting
High-quality trajectories prioritized.
Parameter Efficient
LoRA for low-bandwidth training.
Pulse Native
Seamless integration with SYNNQ.
Ready to Deploy?
ACTIVATE
INTELLIGENCE
Deploy autonomous agents that think and act. Start your pilot program today.
Pilot Program
Limited availability slots
System Demo
Live LAM demonstration