Actions
The decision-making brain for autonomous agents that combines vision, language, and state into intelligent action
Making Machines Think and Act
LAM bridges the gap between language models and action models — enabling intelligent agents that don't just think, but act responsibly, locally, and intelligently across regulated environments.
Autonomous Robots & Drones
Split-second tactical decisions combining visual input, instructions, and telemetry for intelligent navigation and task execution.
Smart Manufacturing
Real-time reaction to live sensor data in factories and logistics systems with continuous operational improvement.
Defense & Security
Fused intelligence from visual, language, and state inputs for action planning in mission-critical scenarios.
Core Capabilities
Everything you need to deploy intelligent, automated decision systems at scale
Multi-Modal Intelligence
Combines vision, language, and sensor data into unified situational understanding — like a pilot using eyes, voice commands, and instruments simultaneously.
Learning by Doing
Learns through trial and reward using reinforcement learning — acquiring real-world behavior, not just facts, like a human pilot learning through flying.
Federated Architecture
Trains locally on each device, shares only compact updates to the global model — ensuring data privacy and regulatory compliance across all nodes.
Safe Optimization
Uses Proximal Policy Optimization (PPO) to explore new actions within safe boundaries — steady improvement without breaking existing knowledge.
Experience-Based Weighting
Prioritizes trajectory quality over data quantity — devices with more completed missions have greater influence on the global model.
Parameter-Efficient Training
Leverages LoRA for low-bandwidth, low-compute training — ideal for embedded devices and constrained environments.
How LAM Works
LAM goes through several phases every time it trains or runs, combining learning, optimization, and federated collaboration
Learning the World
LAM loads specialized AI modules: a vision model (like CLIP) to interpret camera input, a language model (like GPT-2 or LLaMA) to understand instructions, and a state model to read internal sensors. These three parts fuse together to form complete situational understanding — like a pilot using eyes, voice commands, and instrument panels all at once.
Training by Doing
LAM doesn't just read data — it acts. It learns by trial and reward through Reinforcement Learning: taking an action, receiving feedback (reward or penalty), and adjusting its internal policy to make better choices next time. This is how it learns behavior, not just facts — much like how a human pilot learns through flying, not reading manuals.
Optimizing Decisions
LAM uses PPO (Proximal Policy Optimization) — a safe way for AI to explore new actions without making big, unstable jumps. Like giving your team freedom to experiment within defined limits that protect the company brand, this ensures the model improves steadily without breaking existing knowledge.
Working in Federated Mode
LAM is part of the SYNNQ Pulse federated learning network. Each robot, drone, or device trains locally on its own data. Only small updates (not raw data) are sent to the central server. The server merges all updates to improve the global model. This allows continuous improvement across thousands of devices while maintaining data privacy and regulatory compliance.
Smart Aggregation
Instead of weighting updates by number of data samples, LAM uses trajectory weighting — the more real-world missions or "episodes" a device completes, the more influence it has on the global model. This ensures experience and quality of training matter more than raw data quantity.
Iterative Global Improvement
The cycle repeats: devices train locally on new experiences, send compact updates to the central aggregator, the global model improves, and updated policies are redistributed to all devices. In each round, the system becomes more capable — smarter decisions, faster responses, higher safety margins.
How It Looks in Practice
Factory A - Germany
Autonomous robots handle quality inspection on high-volume production lines. They learn to identify defects in optimal lighting conditions and develop efficient inspection patterns for standard materials.
Factory B - Poland
Robots work with varied lighting and handle specialty materials. They develop strategies for detecting subtle defects under challenging conditions and adapting to different product types.
Collective Intelligence
When their experiences are combined on the server, both factories benefit — Factory A learns how to handle varied lighting and specialty materials, while Factory B learns more efficient inspection patterns — without ever sharing proprietary production data or sensitive quality control information.
This is collective intelligence — decentralized, privacy-safe, and continuously improving across your entire manufacturing network.
Key Business Takeaways
What LAM means for your business and strategic operations
Multi-Modal Learning
Understands vision, text, and sensor input together — enabling full-context automation
Reinforcement Learning (PPO)
Learns from actions and feedback, not just static data — ideal for robotics and decision-making systems
Federated Architecture
Enables distributed learning across devices — compliant with data protection and defense regulations
Trajectory-Based Aggregation
Prioritizes real operational experience — smarter weighting of field data
Parameter-Efficient LoRA Training
Lower bandwidth and compute costs — ideal for constrained or embedded devices
Pulse SDK Integration
Seamlessly deployable within SYNNQ Pulse for monitoring, aggregation, and secure AI governance
Collective Intelligence,
Zero Data Sharing
LAM enables sovereign, compliant intelligent agents for defense, industry, and smart mobility — learning from thousands of devices while maintaining complete data privacy and regulatory compliance.
Unified model that can see, understand, and act
Collective intelligence without sharing sensitive data
Continuous improvement across thousands of devices
Compliant with data protection and defense regulations
Lower bandwidth and compute costs for deployment
Seamless integration with SYNNQ Pulse infrastructure
Why This Is Strategic
Bridges the Gap
LAM connects language models with action models, creating systems that don't just process information — they make intelligent decisions and act on them.
Sovereign AI
Enables Europe and regulated regions to build compliant, sovereign intelligent agents for defense, industry, and critical infrastructure.
Responsible Action
Makes machines not just think — but act responsibly, locally, and intelligently within defined safety and regulatory boundaries.
"LAM makes machines not just think — but act responsibly, locally, and intelligently."
Built for organizations that need autonomous systems capable of real-world decision-making while maintaining complete compliance and data sovereignty.
Deploy Intelligent Agents That Learn by Doing
Experience the power of federated reinforcement learning with LAM — where your autonomous systems improve continuously while maintaining sovereignty and compliance.
Request a DemoLet's Build Together
Discover how LAM can power your autonomous systems with federated intelligence