The Constraint
Most modern AI deployments assume abundant memory, floating-point compute, or external resources. Many embedded systems operate without those luxuries.
On-device execution
BitTrace is built for on-device inference in constrained environments. Compact deterministic models execute locally with behavior suited to embedded condition monitoring and sensor-based classification.
Capabilities
- Few-kilobyte inference models for constrained microcontroller-class targets
- Compact deterministic classifiers for sensor data
- Deterministic execution without external runtime dependency
- Engineered for constrained memory and compute environments
- Practical fit for embedded and industrial edge systems
BitTrace API
The BitTrace API has two core concepts: Forge and Scout. Forge builds compact deterministic models from sensor data. Scout is the deployed few-kilobyte inference model that runs on constrained hardware.
Cost and competitive advantage
Many embedded product lines cannot justify larger hardware, cloud dependency, or heavyweight inference stacks.
BitTrace enables compact deterministic inference on constrained systems, helping teams add intelligence without forcing unnecessary hardware escalation.
Engagement
Engagement typically begins with a technical fit review covering target hardware, signal domain, memory limits, compute constraints, and deployment environment.
Qualified projects may proceed into evaluation, pilot development, integration support, and commercial production licensing.
Application areas
- Industrial condition monitoring
- Vibration and sensor-based classification
- Predictive maintenance systems
- Embedded industrial IoT
- Automotive sensor diagnostics
- Low-resource MCU deployments