How much does quantization hurt accuracy?

INT8 quantization typically costs 0.5–2% accuracy on classification tasks. For detection and generation tasks the range is wider. We measure the actual delta against your eval set during week 1 — you get a number, not a range, before any optimization work starts.

Which chips are you optimized for?

Apple Neural Engine (A/M series), Qualcomm Hexagon DSP, Arm Ethos NPU, NVIDIA Jetson (TensorRT), and Intel OpenVINO targets. For MCU deployments: STM32, Nordic nRF, and ESP32 series. We'll tell you in discovery if your target chip has enough headroom for the model you need.

How do you handle model updates without an app release?

OTA weight delivery via a signed CDN (weights only, not code) with a rollback mechanism and a version gate that prevents a degraded model from shipping. Wired into your existing CI so a model improvement triggers an OTA push the same way a dependency update triggers a build.

Edge AI & Embedded Inference

Edge AI & Embedded Inference — AI That Runs When the Cloud Doesn't

Cloud AI inference costs $0.002 per call, adds 200ms of latency, and fails the moment connectivity drops. For IoT devices, factory-floor systems, and mobile apps that need real-time AI, on-device inference isn't a nice-to-have — it's the architecture.

Cloud inference vs. edge inference

Dimension	Cloud inference API call per request	Edge inference On-device, zero network
Latency	150–400ms (round trip)	5–50ms on-device
Works offline
Cost at scale	$0.002+ per call	Hardware amortised
Data privacy	Data leaves device	Nothing leaves device
Model updates	Instant (server-side)	OTA pipeline needed
Right for	Low-frequency, large models	Real-time, always-on, IoT

Most production systems combine both — edge for latency-critical paths, cloud for heavy lifting.

What you get

Model quantization and compression pipeline — INT8/FP16 quantization, pruning, and distillation to fit your target hardware without sacrificing the accuracy you actually need

On-device runtime for iOS (Core ML, BNNS), Android (NNAPI, TFLite, MediaPipe), embedded Linux (ONNX Runtime, TensorRT, OpenVINO), and MCUs (TensorFlow Lite Micro, CMSIS-NN)

Offline inference with cloud sync — graceful degradation when connectivity drops, sync when it returns, with a defined consistency model

Hardware-specific optimization for NPUs, GPUs, and DSPs — Apple Neural Engine, Qualcomm Hexagon, Arm Ethos, NVIDIA Jetson

Benchmarking suite: latency (p50/p95), throughput, power draw, and thermal envelope against your real hardware — not simulator runs

Continuous model delivery pipeline — OTA updates for model weights without requiring an app store release

When it fits

Inference latency under 50ms is a product requirement — cloud round-trips can't meet it
The device is intermittently or never connected (industrial, rural, in-vehicle, wearable)
Per-call cost at your inference volume makes cloud APIs the wrong unit economics
User privacy requires data to stay on-device — no raw sensor data leaves the device

When it doesn't

The model genuinely needs 70B+ parameters and the device can't carry a quantized version without losing the accuracy the use case requires
Inference happens at most a few times per day — cloud is cheaper and simpler at that frequency
You don't own the hardware — if it's a generic browser, on-device inference is limited to WebAssembly ONNX and typically not worth the effort

Process

Week 1: hardware survey and accuracy-vs-size trade-off analysis. Weeks 2–4: model quantization and initial on-device port. Weeks 5–7: hardware-specific NPU/GPU optimization and benchmarking. Weeks 8–10: offline/sync strategy, OTA delivery pipeline, power and thermal testing.

Full delivery process

Pricing

Fixed-price by platform and model complexity. Single-platform port: $60–140k. Multi-platform with OTA delivery: $120–280k. Hardware-specific NPU optimization: add $30–60k per chip target. We'll tell you in discovery if the model can actually run on your hardware at the accuracy you need.

See engagement models

Industries we serve with this

Manufacturing & Industry 4.0

Healthcare

FAQ

How much does quantization hurt accuracy?: INT8 quantization typically costs 0.5–2% accuracy on classification tasks. For detection and generation tasks the range is wider. We measure the actual delta against your eval set during week 1 — you get a number, not a range, before any optimization work starts.
Which chips are you optimized for?: Apple Neural Engine (A/M series), Qualcomm Hexagon DSP, Arm Ethos NPU, NVIDIA Jetson (TensorRT), and Intel OpenVINO targets. For MCU deployments: STM32, Nordic nRF, and ESP32 series. We'll tell you in discovery if your target chip has enough headroom for the model you need.
How do you handle model updates without an app release?: OTA weight delivery via a signed CDN (weights only, not code) with a rollback mechanism and a version gate that prevents a degraded model from shipping. Wired into your existing CI so a model improvement triggers an OTA push the same way a dependency update triggers a build.

Ready to talk edge ai & embedded inference?

30-minute scoping call. No obligation, no hard sell.