Sensing, inference, and device response
Put intelligence where decisions happen.
A device can detect more than a fixed threshold reveals—but an AI output only matters when the hardware, firmware, and control logic can use it reliably. We scope the sensing, inference, and decision path around the product’s real constraints.
Explore applicationsWhere it earns its place
Turn complex signals into useful decisions.
Embedded AI can identify patterns across complex sensor inputs to inform control responses, alerts, or operator recommendations. Its value depends on representative data and performance under the device’s operating conditions.
Recognize patterns
Classify events across noisy or variable sensor inputs so the product can distinguish meaningful changes from normal operation.
Guide a response
Use model outputs to inform control behavior, trigger an alert, or support an operator recommendation—with explicit limits and fallback behavior.
Monitor system behavior
Identify data patterns that may signal a changing equipment condition, directing attention for investigation rather than claiming an automatic diagnosis.
Engineering the whole path
The model is one component of the system.
Signal quality shapes what a model can interpret; compute and timing determine when results are available. We design how the firmware and hardware use each output and test that behavior under the device’s operating conditions.
Signals + data
Sensor placement, sampling, and label assumptions shape what patterns a model can learn; operating variation and missing or corrupt inputs can limit what it can recognize.
Confirm whether the available signals are sufficient to inform the intended product response.
Compute placement
Compare inference on the edge device with inference on connected or cloud compute; right-size the model for memory, compute, and power, favor low-latency options when fast response matters, and account for connectivity and model updates.
Choose a model and compute path that meets the required response time while fitting memory, power, connectivity, and model-update constraints.
Decision + control
Map model outputs to firmware states, actuator limits, and operator guidance. Define how uncertain or variable outputs are bounded, with deterministic fallback behavior.
Define when an inference may influence control—and when it must not—and how the product responds when an output is uncertain.
Evaluation + revision
Use scoped tests—including repeated runs of the same inputs—to evaluate edge conditions, changing inputs, output variability, false alarms, and missed events.
Make performance, output variability, and limitations visible before wider integration.
Across the interfaces
Model outputs need a clear path through the system.
A model result is not a command by default. Timing, confidence, actuator limits, and fault states determine whether software uses it to adjust control, guide an operator, or take no action.
From idea to scoped work
Start with the product need, not the model.
Name the behavior
Share the product goal, what you know about its signals and behavior, and what remains uncertain. An existing dataset or finished specification is not required to start the conversation.
Find a path forward.
When needed, compare rule-based and model-based options, inspect data and compute constraints, and define what a focused proof of feasibility would have to show.
Lay out the approach
We present the recommended scope, evaluation criteria, deliverables, cost estimate, schedule, and expected delivery. The work may focus on one question or extend through device integration and testing.
Start with the technical need
Bring us the product question you’re still working through.
Tell us what you’re trying to learn from the product’s signals. We’ll work with you to identify what’s worth evaluating and outline a bounded next step.
No completed specification is needed for an initial conversation. Tell us what is known and where the uncertainty sits.
Discuss a project