Edge AI
When latency, connectivity, or privacy rules out a cloud round-trip, we optimize and deploy models to run directly on-device — cameras, sensors, mobile hardware — without sacrificing the accuracy your use case needs.
<50ms
Avg. Inference Latency
-80%
Model Size Reduction
10k+
Devices Deployed To
Yes
Offline Capable
What's Included
Optimized model package (quantized/compressed)
On-device benchmark report (latency, accuracy, power)
Deployment package for target hardware
How We Work
01
Hardware Assessment
We evaluate the target device's compute, memory, and power constraints.
02
Model Optimization
We quantize and compress the model to fit device constraints while preserving accuracy.
03
On-Device Testing
We validate real-world performance directly on target hardware, not just in simulation.
04
Deployment & Updates
We ship the model with a pipeline for pushing future updates to deployed devices.
What You'll Receive
- Optimized model package (quantized/compressed)
- On-device benchmark report (latency, accuracy, power)
- Deployment package for target hardware
- Over-the-air model update pipeline
- Fallback strategy for offline conditions
How Engagements Typically Work
Every project starts with scoping — here's the shape most engagements for this service take.
Feasibility Test
A 2-3 week test optimizing a model on sample hardware to validate the latency and accuracy trade-off.
Production Build
Full optimization and deployment pipeline across your device fleet — typically 6-10 weeks.
Ongoing Model Updates
Monthly retainer to ship improved models to deployed devices over the air.
Technologies We Use
The tools and platforms our Edge AI team works in day to day.
Edge Optimization
Infra
Frequently Asked Questions
Cameras, industrial sensors, mobile phones, and embedded boards are all common targets — we scope compatibility during the hardware assessment.
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