On Device AI

Bring intelligence to the edge—optimized, monitored, and governed on-device inference engineered for low latency, privacy, and resilience.

Bring intelligence to the edge—optimized, monitored, and governed on-device inference engineered for low latency, privacy, and resilience.

On Device AI enables real-time intelligence where latency, privacy, or connectivity make cloud inference impractical.

We optimize models for edge constraints and implement lifecycle controls including evaluation, monitoring, versioning, and safe rollout strategies.

Model optimization for edge constraints

On-device inference pipeline integration

Runtime monitoring & drift visibility

Versioning & safe model rollouts

Data capture & feedback loops

Governance & control mechanisms

Real-time edge decisions with low latency and offline support

Governed model lifecycles across fleets

Lower risk through observable and auditable AI behavior

Edge feasibility assessment

Model packaging and runtime selection

Monitoring and drift strategies designed upfront

Versioning pipelines integrated with OTA governance

Production validation in real-world operational conditions

Client Impact

See how we have delivered measurable outcomes for organizations across industries.

Ported ADAS from Linux to QNX and accelerated edge inference on Texas Instruments hardware, integrating perception sensors and runtime frameworks.

Automated benchmarking and quantization workflows across SDK/hardware combos with CI/CD, versioning discipline, and performance analytics.

Automated progressive meter reading from mobile‑captured images using OCR and on‑device models, with full admin and service tooling.

Combine edge intelligence with secure device platforms and fleet governance

Build devices capable of running reliable on-device intelligence.

Govern AI-enabled fleets with OTA updates and telemetry.