West Edge AI runs small, optimized language models and autonomous agents directly on the network and compute edge—powering real-time decision-making for devices, robots, and critical systems. Experience sub-50ms latency, on-prem autonomy, and complete data sovereignty. No cloud dependence. No bottlenecks. Just fast, local, reliable intelligence.
Everything you need to build, deploy, and scale intelligent automation
Run agents and models directly on the edge for sub-50ms decisions—perfect for robotics, devices, and real-world systems that can’t wait for the cloud.
Execute tools locally with our MCP engine—file operations, databases, robotics actions, APIs, sensors, and more. Agents don’t just think; they act.
Agents learn from experience with on-device memory systems inspired by ReasoningBank and Memento—improving over time without retraining.
Configure, instruct, and refine agents using simple language. No complex prompts or engineering expertise required.
All inference runs locally. No data leaves your network. Built on strict European privacy principles for full autonomy and compliance.
Edge systems operate continuously—even offline. Autonomous agents run 24/7 with deterministic performance and no cloud dependencies.
Combine small models with symbolic planning to build precise, domain-specific workflows for industrial, robotics, and enterprise automation.
Deploy specialized agents that coordinate, debate, and share context—enabling swarm robotics, distributed systems, and complex automation.
Scale from one device to thousands of edge nodes with tiny models that run efficiently on CPUs, small GPUs, and embedded hardware.
Get started in minutes with our simple 5-step process
Describe the workflow, behavior, or real-world action you want—whether it’s automating a system, powering a device, or controlling a robot.
Choose your target hardware: on-prem servers, embedded devices, robotics controllers, or edge nodes. Your small model runs locally with sub-50ms latency.
Connect local MCP tools, APIs, databases, sensors, or robotic actuators. Agents gain the ability to perceive, reason, and act in real time.
Agents operate autonomously using on-device memory and feedback loops—learning from experience and improving without retraining.
Add specialized agents, enable multi-agent collaboration, and expand to fleets of devices or robots—all with full data sovereignty and no cloud dependence.
The scientific community is uncovering the same challenges we solve: real-time autonomy, small-model efficiency, memory-driven agents, and edge intelligence.
"Small, well-trained models often outperform much larger models on agentic tasks, showing that scale alone is not the path to autonomy."
"Agents improve most when they learn from their own experiences. Memory, not just model size, is the real driver of long-term reasoning."
"LLMs alone are too slow and unreliable for real-time decision systems. Safe autonomy requires local inference and predictable latency."
"Tiny models with recursive reasoning can outperform models hundreds of times larger on structured logic tasks—efficiency beats scale."
"Centralized AI architectures risk undermining autonomy. Distributed, local systems better preserve human agency and trust."
"For embodied systems and robotics, latency must be measured in milliseconds. Cloud-first AI approaches fundamentally cannot meet this requirement."
Pay only for the edge reasoning capacity you deploy. Keep data local and scale as you grow.
For evaluation, prototypes, and early pilots
For small businesses running private data workloads on-prem
For regulated environments and larger deployments
Everything you need to know about running AI at the edge.
Whether you’re deploying small language models, automating on-prem workflows, or reducing cloud LLM costs—our team will help you get started.
Questions? Reach out to our team:
westedgeaillc@gmail.com