Standard LLMs guess. LogitScore knows when it doesn't know. Deploy quantized, offline, multimodal AI to autonomous systems with mathematically guaranteed safety thresholds.
Inference is scored before a motor command is allowed to execute.
> VLM detects semantic-visual mismatch
> confidence threshold exceeded
> defer to operator / fallback tool
Deterministic robotics requires confidence scoring inside the inference loop, not after the fact.
In the cloud, an AI hallucination is a bad email. In the physical world, a confident guess results in a hardware crash. API-based guardrails add 500ms+ of latency—too slow for real-time motor control.
LogitScore monitors inference at the silicon level. By measuring thermodynamic token entropy in real-time, the runtime triggers a microsecond hardware-level halt before an unsafe action is executed.
The runtime is designed for industrial robotics, autonomous systems, and defense workloads where latency, power, and certainty all matter.
If visual or semantic uncertainty spikes > 85%, LogitScore instantly locks the execution loop and defers to a human operator or fallback tool.
Built for Vision-Language Models. Fuses camera sensor data with local semantic reasoning without leaving the device.
Operates entirely offline on embedded silicon (NVIDIA Jetson, ARM, NPUs). Optimized for Size, Weight, and Power constraints in defense and industrial sectors.
Simulate how deterministic routing differs from a standard inference path when entropy spikes inside a robotics control loop.