Real-time AI for systems where milliseconds matter. Runs on existing hardware.
100-500ms
Network latency, queuing, and round-trip delays make cloud AI unusable for real-time control.
1-50ms
Power hungry, heat generating, and still too slow for microsecond-critical applications.
≥5μs
Deterministic inference on standard edge hardware. No cloud dependency.
Industries where latency isn't a metric—it's a safety requirement.
Real-time perception and control for industrial automation, collaborative robots, and autonomous mobile platforms. ISO 13849 designed for safety-critical motion planning.
AI-assisted diagnostics and surgical systems requiring deterministic latency. Designed for regulatory pathways with safety-first architecture.
Vehicle perception, drone navigation, and industrial autonomous systems. Multimodal sensor fusion at speeds matching physical world constraints.
≥5μs inference timing designed for real-time control loops. Predictable, low-latency performance.
Standard ARM and x86 edge processors. No cloud dependency. Full inference runs locally.
Vision, audio, sensor fusion. Process multiple input streams simultaneously.
Optimize your models for our inference engine. Integration support available.
CycleCore inference integrates into your existing edge architecture. We work with your engineering team to optimize deployment for your specific hardware and latency requirements.
#include <cyclecore/engine.hpp>
// Load inference models
auto engine = cyclecore::Engine::load("/path/to/models");
// Single inference — safety decision
auto result = engine->infer("safety-classifier",
{0.1f, 0.2f, 0.0f});
if (result.is_veto()) {
trigger_safety_stop();
}
// Response in microsecondsOur inference architecture is built on peer-reviewed research and validated benchmarks.
We work with enterprise partners building the next generation of intelligent systems. Request a technical brief or schedule an architecture review.