Secure device identity
Hardware-backed keys, per-device certificates, attestation and automated rotation across the fleet.
Connected Systems
From sensor to decision, engineered as one system
IoT projects stall in the gap between the device team and the cloud team. We own the whole path — firmware, connectivity, provisioning, edge processing, ingestion, analytics and the operator interface — so the data arrives reliably and someone acts on it.
0+
Connected platforms delivered
0M
Devices under management
0%
Average unplanned-downtime reduction
Overview
Fleet-scale operations, not a proof of concept on a bench
The hard problems in IoT are operational: provisioning ten thousand devices securely, rotating certificates, updating firmware without bricking units in the field, and handling the small percentage that will always be offline or misbehaving.
We design for that reality from the start — per-device identity, staged over-the-air updates with automatic rollback, store-and-forward buffering, and observability that tells you which devices are unhealthy before a customer does.
At the edge we push filtering, aggregation and inference close to the sensor, so you pay for bandwidth on insight rather than raw telemetry, and control loops keep working when the link drops.
Safe OTA updates
Signed images, A/B partitions, canary cohorts and automatic rollback on failed health checks.
Edge intelligence
Local filtering, aggregation and on-device ML so decisions survive an unreliable network.
Fleet observability
Connectivity, battery, firmware version and data-quality health scored per device and per site.
What we build
Capabilities you get on day one
The components below are engineered patterns we have shipped repeatedly — not concepts we would be exploring for the first time on your project.
Device management plane
Provisioning, grouping, configuration, firmware rollout and remote diagnostics for fleets of any size.
Time-series pipeline
High-cardinality ingestion, downsampling, retention tiers and query performance tuned for dashboards.
Predictive maintenance
Failure-mode models on sensor history that raise work orders before the asset stops, not after.
Digital twin
A live model of asset state and hierarchy that simulation, analytics and operators share.
IoT security
Secure boot, encrypted transport, network segmentation, anomaly detection and penetration testing of the device itself.
Operator experience
Site and asset dashboards, alarm management with suppression logic, and mobile access for field crews.
Capabilities
Everything inside our iot & connected engineering practice
The full scope of the practice. Engagements typically draw on a focused subset — this is the bench you have access to.
Devices & Embedded
- Embedded firmware development
- RTOS & bare-metal engineering
- Smart device & product development
- Sensor selection & integration
- Low-power & battery optimisation
- Hardware bring-up & board support
- BLE, LoRaWAN, NB-IoT & Zigbee
- Device certification support
Edge & Connectivity
- Edge computing architecture
- Edge AI & on-device inference
- IoT gateway development
- MQTT & protocol engineering
- OPC-UA & Modbus industrial integration
- Store-and-forward & offline resilience
- Device provisioning at scale
- Over-the-air update infrastructure
Industrial & Vertical IoT
- Industrial IoT (IIoT) & SCADA integration
- Predictive maintenance platforms
- Condition monitoring & vibration analysis
- Energy & utility monitoring
- Healthcare IoT & connected devices
- Cold chain & logistics tracking
- Smart city & public infrastructure
- Smart building & facility automation
Platform & Applications
- IoT cloud platform engineering
- Time-series data architecture
- Real-time operational dashboards
- Digital twin modelling
- Alerting & escalation workflows
- Fleet management portals
- Mobile companion applications
- IoT security assessment
Business impact
The outcomes clients measure
Figures are medians across delivered engagements in this practice. We will baseline your own numbers during discovery rather than promise these.
31%
Less unplanned downtime
Condition monitoring and predictive alerts converting breakdowns into scheduled interventions.
↓ 78%
Lower telemetry cost
Edge aggregation and adaptive sampling replacing raw high-frequency uplink.
↑ 22%
Better asset utilisation
Visibility into real duty cycles and idle time across sites and shifts.
0
Field truck-rolls for updates
Staged OTA with rollback removes on-site firmware visits entirely.
Technology stack
IoT & Connected Engineering technology stack
Selected per engagement against your existing estate, your team's skills and total cost of ownership — never by partnership tier.
Embedded
- C / C++
- Rust
- Zephyr RTOS
- FreeRTOS
- ESP-IDF
- STM32
- Nordic nRF
Edge
- Azure IoT Edge
- AWS Greengrass
- K3s
- Docker
- TensorFlow Lite
- ONNX Runtime
Connectivity
- MQTT
- CoAP
- LoRaWAN
- BLE
- NB-IoT
- OPC-UA
- Modbus
Cloud IoT
- Azure IoT Hub
- AWS IoT Core
- Google Cloud IoT
- ThingsBoard
- EMQX
Data
- TimescaleDB
- InfluxDB
- Azure Data Explorer
- Apache Kafka
- Delta Lake
Visualisation
- Grafana
- Power BI
- React dashboards
- Azure Digital Twins
How we deliver
How a iot & connected engineering engagement runs
Six stages, each with a defined output. You can stop after any one of them and still hold something useful.
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Use-case & feasibility
Define the decision the data must support, then work backwards to sensing, sampling rate, connectivity and power budget.
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Architecture & prototype
Reference device, connectivity trial in the real environment, data model and security design validated on site.
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Pilot deployment
A limited fleet at one or two representative sites, instrumented for connectivity, data quality and operator feedback.
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Platform hardening
Provisioning automation, OTA pipeline, fleet observability, alarm tuning and security assessment.
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Scale rollout
Site-by-site deployment with installation runbooks, commissioning checks and field-team training.
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Operate & optimise
Fleet health management, model retraining, firmware lifecycle and expansion into adjacent use cases.
Industries we serve with this practice
Engagement models
How to start with IoT & Connected Engineering
Three commercial shapes. Most clients begin with an assessment and move into delivery once the plan is agreed.
Fixed-price assessment
From $12,000
Two to four weeks. Produces a prioritised backlog, target architecture, risk register and a costed delivery plan you own outright.
- Named architect
- Executive readout
- No obligation to proceed
Most common
Milestone delivery
Scoped per phase
Well-bounded phases priced against agreed acceptance criteria. Suited to migrations, integrations and defined product increments.
- Acceptance-based billing
- Change control included
- Fortnightly demos
Dedicated pod
Monthly retainer
An embedded team — lead, engineers, QA — working in your sprints and tooling with US-hours overlap from our India centre.
- Scale up or down monthly
- Your definition of done
- Direct team access
Indicative ranges for planning purposes. Final pricing follows scope confirmation — we do not quote before we understand the problem.
FAQs
IoT & Connected Engineering — frequently asked
Can you work with our existing equipment?
Usually. Industrial assets often expose data through OPC-UA, Modbus, CAN bus or a PLC, and where nothing is available we retrofit external sensors — vibration, current, temperature, acoustic. A short feasibility survey on site establishes what is realistically obtainable before we commit to a design.
How do you keep devices secure in the field?
Per-device identity with hardware-backed keys where the platform supports it, secure boot and signed firmware, mutual TLS, no shared credentials, network segmentation away from IT systems, and anomaly detection on device behaviour. We also penetration test the device and its update path, because physical access is part of the threat model.
What happens when connectivity drops?
The device keeps working. Local control loops continue, telemetry is buffered to persistent storage with a retention policy, and data is forwarded with deduplication when the link returns. Any design that assumes reliable connectivity will fail in a real industrial environment.
Cloud or edge processing?
Both, split by latency and cost. Safety and control decisions requiring sub-second response stay at the edge. Fleet-wide analytics, model training and long-term storage belong in the cloud. Edge aggregation typically cuts bandwidth cost by an order of magnitude versus streaming raw sensor data.
Do you build the hardware too?
We do firmware, board support and integration, and we work with established design and contract manufacturing partners for enclosure, PCB and production. For most clients the right answer is industrial off-the-shelf hardware with our firmware and platform, rather than a custom board.
Related practices
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Data Engineering & Analytics
Lakehouse platforms, ETL/ELT pipelines and governed analytics that turn scattered systems into one trusted layer.
Cloud Services
Migration, cloud-native engineering, Kubernetes, FinOps and resilience across AWS, Azure, Google Cloud, Oracle and IBM.
Emerging Technologies
AR/VR/MR, digital twins, spatial computing, robotics, edge AI and quantum-readiness advisory.
Connected Systems
Ready to talk about iot & connected engineering?
Send the context — current systems, constraints, what you have already tried. An architect from this practice will reply, usually within one business day.