Capability 05
Embedded, Edge & Applied AI
We put applied intelligence where the system actually operates: on the device, at the edge, in the cloud, or across all three, with the architecture, latency, connectivity and reliability worked out first.
When this work is needed
The situations that bring a business to this capability.
- A product or machine produces signals that a person can interpret and the business wants that interpretation to happen automatically, at the point of operation.
- A decision has to be made locally, in milliseconds or without a network, and sending everything to the cloud is not an option.
- Telemetry from a fleet contains patterns that predict faults, and nobody is looking at it in that way.
- An operation has a repetitive judgement step (inspection, classification, triage) that is a candidate for automation, with a person kept in the loop.
What we engineer
- 01Architecture for inference
- Where the model runs and why: on a microcontroller, on an edge gateway, in a regional cloud, or split between them, decided by latency, connectivity, cost, power and what happens when a part is unavailable.
- 02Embedded and edge inference
- Models sized and quantised for the processor they run on, integrated with the device's firmware and sensors, and updated through the same fleet mechanisms as the rest of the software.
- 03Machine vision, classification and anomaly detection
- Vision and signal models trained on the product's own data, with the collection, labelling and validation designed as part of the system rather than left to chance.
- 04Decision support and intelligent automation
- Models placed inside operational workflows so that their output becomes an action, a flag or a recommendation with a clear owner, and their performance is measured in service.
Technical scope
The scope of a project is agreed in writing before work starts. These are the areas this capability normally covers.
- ARINC 429 integration for aerospace and aviation systems
- Embedded inference on constrained processors
- Local and edge AI, remote and cloud inference
- Hybrid edge and cloud architectures
- Machine vision and image classification
- Signal classification and anomaly detection
- Telemetry interpretation and predictive indicators
- Intelligent automation and decision-support systems
- Specialist domain models on the business's own data
- Model deployment, monitoring and update in the field
Constraints we design for
- Latency and connectivity
- A decision that has to be made in the loop is made on the device. The architecture states the round-trip budget and the offline behaviour before a model is chosen.
- Reliability over novelty
- A model is a component with a failure mode. Confidence thresholds, fallbacks and human escalation are designed in, and a simpler method is used where it is good enough.
- Data provenance
- Training and validation data comes from the actual product and operation, with its origin recorded, so that performance claims can be checked and the model can be retrained.
- Deployment reality
- Processor, memory, power, temperature and update mechanisms are fixed by the product. The model is engineered to fit them, not the other way round.
Where it applies
Sectors whose systems this capability is engineered for.
- Industrial IoT
Sensors, gateways, edge systems and telemetry feeding operational platforms and the business systems around them.
- Automation, Robotics & Autonomous Systems
Integration of sensing, control interfaces, data, edge intelligence and the operator systems that supervise them.
- Aerospace & Aviation
Systems where reliability, integration, operating constraints and distributed data all matter at once.
- Connected Consumer Products
Embedded hardware, connectivity, companion software and service platforms that together make up the ownership experience.
Related capabilities
01
Connected Products
Turn physical products into connected digital products.
- Telemetry
- Remote monitoring
- Device-to-cloud
06
Resilient & Off-Grid Digital Systems
Digital systems designed to operate beyond reliable fixed infrastructure.
- Intermittent connectivity
- Store-and-forward
- Local-first architecture
04
Custom Operational Systems
Software engineered around specialist operations where generic platforms do not fit.
- Workflow systems
- Production and service
- Field operations
Discuss a project
If a product, operation or business system spans devices, software, data and real-world constraints, we can help define and engineer the system around it.