IoT & Edge AI
“Intelligent Devices That Make Money”
Intelligence is most valuable where the data is born, on the factory floor, in the vehicle, beside the patient. We push ML inference to the edge so decisions happen in milliseconds, keep working when the network doesn't, and scale cleanly from a single prototype to thousands of devices under secure, central control. Provisioning is zero-touch, updates ship over the air, and a digital twin keeps you in sync with every device in the field.
Some decisions can't wait for the cloud.
The cloud is the wrong place to make a decision that has to happen in ten milliseconds on a factory floor. By the time sensor data makes the round trip, the defective part is already down the line, the machine has already failed, the moment has passed. And the moment connectivity drops, a cloud-dependent device becomes an expensive brick.
Running intelligence on the device solves the latency and resilience problem but creates new ones. How do you update a model on ten thousand devices safely, provision them without sending a technician to each one, and keep them secure when they live outside your network perimeter? Edge AI is as much an operations problem as a machine-learning one.
Intelligence at the edge, operated at scale.
We push inference to where the data is born and design for the network not being there. Models run locally for millisecond decisions, buffer telemetry when offline, and sync when the link returns, so a dropped connection degrades gracefully instead of stopping the line.
Around the fleet we build the operations layer that makes thousands of devices manageable: zero-touch provisioning so a device configures itself on first boot, over-the-air model and firmware updates you can stage and roll back, and a digital twin that mirrors every device so you can see and act on the whole fleet from one place.
Everything you need, engineered to production standards.
Low-latency intelligence at the edge, with secure provisioning and fleet operations that scale from a prototype to thousands of devices in the field, resilient even when connectivity isn't.
- ML inference deployment via AWS IoT Greengrass and Azure IoT Edge
- Real-time, low-latency decision-making at the device
- Fleet management for thousands of devices with secure zero-touch provisioning
- Digital twins and over-the-air model & firmware updates
- Edge-to-cloud telemetry with offline-first resilience
- Purpose-built for manufacturing, healthcare, and logistics environments
“Real-time decisions at the device, with the fleet infrastructure to scale them.”
We engineer for production from day one, then transfer ownership so the capability stays with your team.
Built for the problems you're actually facing.
- 01 Real-time quality inspection on a production line
- 02 Predictive maintenance from on-device sensor data
- 03 Connected medical and logistics devices with offline resilience
- 04 Fleet rollout with zero-touch provisioning and OTA updates
The stack we reach for.
Battle-tested tools, chosen to fit your team and constraints, never technology for its own sake.
- AWS IoT Greengrass
- Azure IoT Edge
- Digital Twins
- OTA Updates
- MQTT
- Edge ML
Why teams pick AzeniQ for this.
Offline-first by design
We assume the network will fail and build for it. Local inference and buffered telemetry keep devices working and lose nothing when connectivity drops.
Fleet operations, not just a prototype
Zero-touch provisioning, OTA updates with rollback, and digital twins make the jump from one device to ten thousand a plan rather than a crisis.
Edge-to-cloud as one system
We design the device, the sync, and the cloud backend together, so telemetry, updates, and security fit instead of fighting each other.
Engagements designed to leave you stronger.
Every service follows the same disciplined path: de-risk fast, engineer for production, then transfer ownership.
Frame & de-risk
We pressure-test the goal, define measurable outcomes, and ship a focused proof-of-concept fast.
Engineer to production
Hardened, observable, cost-aware systems built on AWS/Azure/GCP with security by default.
Transfer & scale
We embed the practices and mentor your team so the capability stays in-house.
Answers before you ask.
Which platforms do you build on?
Primarily AWS IoT Greengrass and Azure IoT Edge, chosen to match your existing cloud and hardware. We run inference with the runtime that fits your device's compute and memory budget.
What happens when a device loses connectivity?
It keeps making decisions locally and buffers its telemetry, then syncs automatically when the connection returns. Offline resilience is a design default, not an add-on.
How do you update models across thousands of devices?
Over the air, in controlled rollouts with rollback. You can stage an update to a subset of the fleet, verify it, then expand, the same way you would ship software safely.
Which industries do you work with?
Most often manufacturing, healthcare, and logistics, anywhere real-time decisions at the device unlock efficiency, safety, or new revenue.
More of what we do.
Ready to Build The Future Together?
Tell us where you're headed. We'll map the fastest secure path from idea to production.