Hardware selection and benchmarking
We profile candidate boards and accelerators against your accuracy, latency, power, and cost targets, so the hardware decision rests on measured results rather than spec sheets.
Robotics & Emerging AI
Edge AI development close to the sensor.
When latency, bandwidth, or privacy rule out a round trip to the cloud, the model has to run on the device. Our edge AI development practice compresses, optimizes, and deploys models to cameras, gateways, and microcontrollers, then keeps entire fleets updated safely.
- Works offline
- Jetson to microcontroller
- OTA model updates
01 Overview
Inference in milliseconds, without a cloud dependency
Streaming every video frame or vibration reading to a data center is expensive, slow, and sometimes impossible on a factory floor or a remote site. Edge computing AI flips the pattern: the device makes the decision locally and sends only events or summaries upstream. That cuts bandwidth, keeps raw data on premises, and lets systems keep working when connectivity drops. Edge AI development is how we make that practical on hardware with tight power and memory budgets.
Getting a model from a notebook onto a device is mostly an engineering problem. We apply model quantization, pruning, and distillation, then compile for the target runtime, whether that is TensorRT on NVIDIA Jetson, OpenVINO on Intel, or TensorFlow Lite Micro for TinyML development on microcontrollers. Around the model we build the IoT AI solutions layer: device provisioning, secure over-the-air updates through AWS IoT Greengrass or Azure IoT Edge, and telemetry that shows how each model performs in the field.
02 Capabilities
Edge AI & IoT capabilities.
End-to-end Edge AI & IoT: strategy, engineering and the production operations that keep it working.
Model compression and optimization
Quantization, pruning, and knowledge distillation shrink models to fit limited memory, with accuracy checked after every step so nothing important is quietly lost.
TinyML for microcontrollers
Keyword spotting, anomaly detection, and gesture models that run on Arm Cortex-M and ESP32 chips using TensorFlow Lite Micro or Edge Impulse, often on battery power.
Edge vision pipelines
Multi-camera video analytics with NVIDIA DeepStream or GStreamer, running detection and tracking locally and sending only alerts and metadata to your backend.
Fleet management and OTA updates
Signed model and firmware updates, staged rollouts, and automatic rollback using AWS IoT Greengrass, Azure IoT Edge, or a lightweight custom agent.
Edge-to-cloud data loop
Devices flag uncertain predictions and upload samples for labeling, so models improve continuously from real field conditions instead of stale training sets.
03 How we deliver
From first call to production.
Every Edge AI & IoT engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Predictive maintenance on factory equipment
Vibration and current sensors feed an on-device model that spots early signs of bearing or motor wear and alerts maintenance before failure.
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UC-02
Smart cameras for retail and safety
Cameras count footfall, detect spills, or check PPE locally, keeping video on site while sending anonymized events to a central dashboard.
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UC-03
Connected vehicles and EV chargers
In-vehicle or charger-side models monitor driver attention, battery behavior, or connector faults in real time, even with patchy cellular coverage.
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UC-04
Wearables with on-device health signals
Low-power models classify activity, sleep stages, or irregular heart rhythms directly on the wearable, extending battery life and protecting personal data.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Edge hardware
- NVIDIA Jetson
- Google Coral
- Raspberry Pi
- Intel Movidius
- ESP32
Inference runtimes
- TensorRT
- ONNX Runtime
- TensorFlow Lite
- OpenVINO
- Edge Impulse
IoT platforms
- AWS IoT Greengrass
- Azure IoT Edge
- MQTT
- Balena
06 Industries
Built for your sector.
We deliver Edge AI & IoT for regulated and fast-moving industries alike.
When should we run AI at the edge instead of in the cloud?
Edge makes sense when decisions must happen in milliseconds, when connectivity is unreliable, when bandwidth costs for raw data are high, or when privacy rules discourage sending data off site. Many systems end up hybrid: fast inference on the device, with the cloud handling training, analytics, and fleet coordination.
Will compressing a model hurt its accuracy?
Some compression techniques cost a little accuracy, but the loss is often small when done carefully. We measure accuracy on your own validation data after each quantization or pruning step and stop before results degrade past the threshold you set. Quantization-aware training and distillation can recover much of the gap when needed.
What hardware do you support for on-device machine learning?
We work across NVIDIA Jetson modules, Google Coral, Intel accelerators with OpenVINO, Qualcomm and NXP application processors, and microcontrollers such as Arm Cortex-M and ESP32. The right choice depends on model size, power budget, unit cost, and environment, which is why we benchmark before recommending anything.
How do you update models on thousands of deployed devices?
We set up signed, versioned over-the-air updates with staged rollouts. A new model goes to a small canary group first, telemetry confirms it performs as expected, and then it expands to the wider fleet. If metrics drop, devices roll back automatically to the last known good version.
How do you secure IoT AI solutions?
Security is designed around common IoT requirements: unique device identities, mutual TLS for communication, signed firmware and model artifacts, secure boot where hardware supports it, and least-privilege access to cloud resources. We also encrypt models at rest when intellectual property protection matters and review the attack surface before rollout.