Edge AI Development Services

Sirin Software has built embedded products since 2014, with 100+ projects delivered by our own R&D team. As an edge AI development company, we put the intelligence on the device itself, so inference runs in real time with no cloud round trip. Model, firmware, and board come from one team, which software-only vendors cannot offer.

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Edge artificial intelligence (edge AI) means the ML model runs on the device, an MCU, SoC, or gateway, instead of sending raw data to a server. Edge computing brings the compute to the data, so on-device inference happens in milliseconds, next to the source. Cloud AI still wins at training; AI at the edge wins on latency, privacy, offline operation, and traffic costs.

Our engineers run internal R&D on on-device decision-making, from NPU benchmarks to milliwatt always-on loads. Grand View Research projects the global edge AI market to grow from $30.0 billion in 2026 to $118.7 billion by 2033 at a 21.7% CAGR, driven by demand for real-time processing across IoT and industrial applications, with North America holding a 36% share.

Our Edge AI Development Services

Edge AI Consulting & Feasibility

We start where most edge projects fail: is the use case worth a custom device, and can the model fit the power budget? A short feasibility study scopes your custom edge AI development before you commit to silicon.

AI Model Development & Optimization

Our team tunes models for the constraints of edge inference: small memory, tight power, fixed latency. We then shrink them for the target chip until accuracy and frame rate meet spec on real hardware.

Computer Vision at the Edge

We build edge AI for computer vision: detection, tracking, and classification next to the sensor. Typical jobs are people counting, defect spotting, and driver monitoring, tuned per camera and lighting.

Embedded & Firmware Integration

Our developers wire the model into real firmware: sensor drivers, RTOS or embedded Linux, watchdogs, and OTA updates. The result is a device that survives a power cut, not a demo tied to a laptop.

Edge Hardware Design & Integration

We design the board around the model: NPU selection, camera interfaces, thermals, power rails for battery or PoE. Schematics and PCB layout stay in-house, so the model and the board share one roadmap.

Deployment, MLOps & Support

The fleet side ships too: model versioning, staged OTA rollouts, and drift monitoring. When data changes, you retrain and redeploy without recalling a unit.

Edge AI Use Cases We Deliver

Real-Time Video Analytics & Object Detection

On-device computer vision watches the scene and reports events, not video streams. Sites get real-time inference on people, vehicles, and zones without uploading footage.

Predictive Maintenance & Anomaly Detection

Models read vibration, current, and temperature at the machine and flag drift before failure. Maintenance moves from the calendar to actual equipment condition, where the savings sit.

Driver Monitoring & ADAS

In-cabin models detect drowsiness and distraction; road-facing models handle object detection with sensor fusion across camera, IMU, and GNSS. Alerts fire in the vehicle even without network coverage.

Voice & Audio Processing on-Device

Keyword spotting, voice commands, and acoustic event detection run locally, so audio never leaves the room. That is the difference between a privacy feature and a promise.

Smart Sensors & TinyML

Edge machine learning fits into milliwatts: TinyML models on MCUs classify motion, sound, or gas readings for years on a battery. A sensor that decides locally sends a few bytes instead of raw signals.

Real-Time Quality Inspection

Embedded vision on the production line checks every unit against the spec at line speed. Defects get caught at the station where they happen, rather than at final QA.

Business Benefits of On-Device AI

1

Real-Time Decisions & Ultra-Low Latency

Low latency AI answers in milliseconds because data never travels: the decision happens where the event does. For braking, safety stops, and line rejects, that gap is the whole product.

2

Data Privacy & On-Device Security

Raw video and audio stay on the device; only events and metrics leave it. Regulators and customers read data privacy (on-device) as an architecture they can verify, not a policy they must trust.

3

Offline Operation & Lower Infrastructure Costs

Devices keep working through outages and where coverage never existed. Bandwidth and cloud bills drop with them, since you stream conclusions instead of raw data.

Industries We Serve

Retail

Logistics & Transportation

Warehouse

Agriculture

Manufacturing / Industrial

Healthcare / Wearables

Edge AI Platforms and Hardware We Work With

SoC / NPU / MCU

VIDIA Jetson, STM32N6, Hailo, Renesas RZ/V2H, Alif Ensemble, Alif Balletto, Infineon PSOC Edge, Ambiq, BrainChip AKIDA.

Frameworks

TensorFlow Lite / LiteRT · ONNX Runtime · PyTorch Mobile / ExecuTorch · OpenVINO · Edge Impulse

Optimization techniques

model quantization (INT8/INT4) · model pruning · knowledge distillation · hardware acceleration

How We Develop Edge AI Solutions

Discovery & Feasibilit

We settle the edge vs cloud split and shortlist the SoC before any code: power budget, unit cost, and latency decide the platform.

Data & Model Development

Our team collects and labels field data, then trains against the metric that matters: recall on defects, false alarms per day, frames per second.

Model Optimization

Quantization, pruning, and TinyML techniques shrink the model to fit the chip. We trade accuracy for size in the open, with numbers.

Hardware & Firmware Integration

The model lands in real firmware on the target board, with drivers, memory maps, and update paths built around it.

On-Device Testing

We measure latency, accuracy, and power draw on the physical device in field conditions: heat, vibration, weak networks.

Deployment & MLOps

Fleet rollout with versioning, staged OTA updates, and drift monitoring, so models keep earning their place.

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