AI
AI Product Design
We add intelligence to products so they can see, listen, predict, and act—on the device (edge) or in the cloud. From choosing the right model to validating accuracy and safety, we design AI that fits your cost, power, and privacy needs—and actually ships.
What we build
On-device AI (Edge): tiny-ML for MCUs/MPUs, vision models on NPUs/GPUs, DSP pipelines
Cloud AI: scalable inference APIs, retraining pipelines, data labeling & drift monitoring
Human interface: voice wake/commands, gesture detection, anomaly alerts, smart UIs
Safety & governance: guardrails, bias checks, explainability, and audit logs
Typical use-cases
Quality inspection • Predictive maintenance • Presence/people counting • Gesture/voice control • Document/scanner intelligence • Power/energy optimization • Smart wearables & appliances
Built-in essentials
Accuracy you can trust: curated datasets, test/validation sets, confusion matrix targets
Latency & power: model compression (quantization, pruning), right silicon selection
Privacy & security: on-device processing when needed, encrypted data and model files
OTA & lifecycle: telemetry, A/B rollouts, automatic fallback/rollback
Deliverables
Model cards (accuracy, latency, memory, failure modes)
Edge firmware or runtime container with inference code
Data pipeline + retraining scripts; MLOps templates
Test harness, golden datasets, certification support (EMC/safety for hardware products)
Documentation: APIs, tuning knobs, deployment playbook
How we work
Discover & define: success metrics, constraints, and failure boundaries
Data & baseline: collect/label a starter set; train a quick baseline to prove feasibility
Engineer & optimize: select models/hardware; compress and tune for target latency/power
Validate: field trials; robustness testing (lighting, motion, noise, occlusion); guardrails
Deploy & improve: OTA rollout; monitor drift; scheduled retrains and model refresh
Edge vs cloud—how do we choose?
We balance latency, power, privacy, and connectivity; often a hybrid works best.
How accurate will it be?
We set target metrics up front and iterate with real data and corner-cases.
Can this run on a small MCU?
Yes—tiny-ML is viable for many tasks using quantized models and efficient feature extraction.