Industrial IoT
Validation and optimization of embedded edge platforms supporting industrial monitoring, sensing and intelligent control.
Engineering Service
Embedded & Edge AI Systems
Intelligence engineered for deployment.
Engineering intelligent embedded and edge AI systems through architecture assessment, validation, simulation and performance optimization before deployment — including low-power and energy-constrained system design.
Engineering Process
Edge Inference Pipeline
Sensor data is processed and evaluated at the edge, with cloud synchronization treated as optional rather than assumed.
Engineering Capability
Independent engineering. Evidence‑based decisions.
Edge AI systems that look sound in the lab often underperform once deployed. Independent technical validation, simulation‑driven engineering and vendor‑neutral technical advisory confirm embedded and edge AI systems are reliable, scalable and deployment‑ready before that becomes a costly discovery.
Embedded System Architecture Review
Edge AI Validation & Optimization
Embedded Hardware Validation
System Performance Optimization
Proof-of-Concept Development
Deployment Readiness Assessment
Low-Power & Energy-Constrained Systems
Specialised Capability
Low-Power & Energy-Constrained Systems
Engineering energy behaviour before deployment.
We assess how firmware, operating states, compute workloads, and network behaviour affect the energy profile of embedded and edge devices, supporting battery-life analysis, power-budget decisions, and low-power optimisation.
Measurement Capability
We measure device current from picoamps to amps within a single duty cycle, resolving sleep-state consumption orders of magnitude below general-purpose profilers and allowing leakage to be characterised across supply rails. Measurement campaigns can run uninterrupted for days, including tests through complete battery discharge.
For cellular devices, network conditions are held under laboratory control rather than left to a live operator. PSM and eDRX timers, RRC release behaviour, and repetition count are set by us and held fixed under controlled conditions, separating device behaviour from network behaviour.
These measurements quantify device energy demand under controlled conditions; they do not, by themselves, constitute a battery-life estimate. Translating measured energy into expected service life depends on duty cycle, battery chemistry, and ageing assumptions that sit outside this measurement scope.
Current Engineering Services
Radianode's currently defined engineering engagements for battery-powered cellular IoT — not the limit of what this capability can address, but the engagements available today.
S1
Firmware in the loop characterization
Where does the energy actually go inside one reporting event?
S2
Network condition parametric study
Does the energy budget survive the coverage the devices will meet?
S3
Module comparison
Which module is genuinely cheaper to run, measured the same way?
S4
Energy budget design review
Is the target achievable, and which assumption is most likely to break it?
S5
Method design with client execution
How do we measure this defensibly on the equipment we already own?
Detailed in the RN-SVC-001 Service Portfolio.
Where We Apply This
Where this expertise creates value.
Intelligent embedded and edge AI systems demand high reliability, predictable performance and confident deployment.
Smart Energy
Engineering validation for intelligent energy systems, edge analytics and connected utility infrastructure.
Connected Products
Technical assessment and validation of embedded connected devices before production deployment.
Intelligent Automation
Validation and optimization of embedded control systems and edge AI platforms supporting autonomous industrial operations.
How We Engineer
Every Engagement Is Guided by Four Principles
01
Evidence over assumption
Engineering decisions are supported by objective evidence, not experience alone.
02
Simulation before deployment, where appropriate
Modeling and simulation reduce uncertainty before physical or operational commitment.
03
Vendor-neutral recommendations
Technology recommendations reflect engineering merit, not vendor relationships.
04
Decisions supported by measurable analysis
Conclusions are traceable to data, not opinion — every recommendation can be explained.