IoT & Connected Systems Software
We build the software layer that ingests, processes, and acts on data from connected equipment, sensors, and field devices — designed to tolerate unreliable connectivity and integrate with the operational systems you already run.
- Device & sensor data ingestion
- Real-time data processing
- SCADA/MES & OT integration
- Offline-tolerant field connectivity
- Monitoring & alerting dashboards
IoT and connected systems software is the software layer that turns data from equipment, sensors, and field devices into something a business can act on — ingesting readings from shop-floor machinery, EV chargers, or energy meters, processing them in real time, and feeding dashboards, alerts, and existing operational systems. North Tech Labs builds this software layer specifically: device and gateway data ingestion, processing pipelines, and the applications built on top — not the sensors, hardware, or physical devices themselves.
Business challenges this addresses
- Field devices generate data that never reaches a usable systemSensors and equipment often produce a constant stream of readings that stays trapped in a proprietary interface or local logger, unavailable to the systems and people who could act on it.
- Connectivity in the field is unreliableDevices installed on a factory floor, a vehicle, or a remote site regularly lose connection, and a system built assuming constant connectivity fails or silently drops data when that happens.
- Existing SCADA, MES, and OT systems weren't designed to share dataOperational technology systems often run in isolation, built years before modern integration was a consideration, which makes connecting them to newer software a deliberate engineering problem rather than a plug-in.
- Data volume grows faster than the ability to process itA pilot of a handful of connected devices can produce a manageable stream, but scaling to hundreds or thousands generates a volume and rate of data that an ad hoc pipeline wasn't built to handle.
Capabilities
- Device and sensor data ingestion via gateways and edge collectors
- Support for common IoT protocols, including MQTT, over existing gateways
- Store-and-forward handling for intermittent or offline connectivity
- Device fleet and connection-status monitoring
- Real-time stream processing and event-driven pipelines
- Integration with existing SCADA and MES systems
- Time-series data storage and historical querying
- Alerting and threshold-based automation
- Operator and management dashboards
- Customer- or operator-facing monitoring interfaces
- Reporting on device and asset status over time
- Role-based access to device and operational data
Typical solutions
Examples of the kind of systems this service can build — not a list of completed client projects unless stated otherwise.
- Equipment-monitoring platformA system consolidating data from shop-floor machinery or field equipment into a single dashboard for real-time status and historical analysis.
- IoT data ingestion pipelineA pipeline that collects data from distributed devices or sensors, processes it in real time, and makes it available to downstream systems and dashboards.
- SCADA/MES integration layerA middleware layer connecting operational technology systems with modern applications, so plant or field data reaches the tools that need it without manual export.
- Connected asset dashboardAn interface giving operators visibility into distributed assets — machinery, chargers, meters — with alerting when something needs attention.
- Field-device fleet management systemA system tracking the connection status, configuration, and health of a fleet of deployed devices across sites.
- Offline-tolerant field applicationA system designed to keep functioning, and later reconcile, when a field device or site loses connectivity for an extended period.
Delivery approach
- 1DiscoveryWe map the devices, sensors, and existing systems involved, along with the connectivity conditions the software has to tolerate in practice.
- 2ArchitectureWe design a data-ingestion and processing architecture sized to the expected device count, data volume, and protocol diversity, including how it behaves when devices are offline.
- 3PilotWe validate the approach on a small number of devices or a single site before scaling to the full fleet, so ingestion and integration assumptions are tested against real conditions early.
- 4DevelopmentWe build the ingestion, processing, and application layers in reviewable stages, testing against real or representative device data as we go.
- 5QAWe test the happy path and, deliberately, the failure paths — dropped connections, malformed readings, and device downtime.
- 6ReleaseWe roll out with monitoring in place, so ingestion or integration failures surface as alerts rather than a silent data gap.
- 7Ongoing evolutionWe maintain the system as the device fleet grows, protocols change, or new equipment is added, rather than treating it as a one-time build.
Architecture & engineering considerations
- Offline-tolerant connectivityIngestion designed to expect dropped or intermittent connections from field devices, with store-and-forward handling and reconciliation rather than assuming a constant, reliable link.
- Ingestion scalingA data pipeline sized for realistic device counts and data rates, built to scale as a fleet grows from a pilot of a handful of devices to the full deployment.
- Device and protocol diversityAn architecture that accommodates the reality that not every device speaks the same protocol or reports data in the same format, without hard-coding assumptions that break when a new device type is added.
- Integration with existing OT systemsInterfaces to existing SCADA, MES, and other operational technology systems designed to tolerate those systems' own downtime, proprietary formats, and inconsistent data quality.
- Time-series data storageA storage model appropriate to high-volume, timestamped device data, balancing query performance against long-term retention costs.
- Security of device connectionsAuthentication and access boundaries for device connections and the data they submit, scoped deliberately rather than assuming a trusted network by default.
Where this fits
Is this the right fit?
- A good fit when...You have equipment, sensors, or field devices generating data that isn't reaching the systems or people who could act on it, or an existing IoT pilot needs to scale into a production-grade platform.
- Not a good fit when...The need is for the hardware or sensors themselves — manufacturing, sourcing, or physically installing devices. That's not this; we build the software layer and can work alongside whichever hardware vendor you use or already have in place.
- Typical engagement shapeA scoped ingestion and application layer validated on a pilot site or device set, followed by scaling to the full fleet and ongoing evolution as devices and protocols change.
Related services
- Custom Software DevelopmentDesign and development of secure, scalable custom software for companies across Sweden and the Nordic region.
- API Development & Systems IntegrationAPI design, partner integrations, and systems-integration engineering connecting ERP, MES, WMS, and third-party platforms into a reliable data flow.
- Data Engineering & AnalyticsData pipelines, warehousing, quality validation and BI reporting built to make scattered operational and sensor data reliable enough to trust.
- DevOps & Cloud InfrastructureDevOps engineering for teams with manual, risky deployments: CI/CD, infrastructure-as-code, and observability built as a real discipline.
Frequently asked questions
Do you build the hardware or sensors too?
No. North Tech Labs builds the software layer — data ingestion from existing devices and gateways, processing, and the applications built on top. We don't manufacture sensors, hardware, or physical devices; we integrate with whichever hardware you already have or are sourcing separately.
How do you handle unreliable connectivity from field devices?
We design ingestion to expect dropped or intermittent connections rather than assume a constant link — store-and-forward handling, reconciliation when a device reconnects, and monitoring that surfaces a data gap instead of hiding it.
Can this integrate with our existing SCADA/MES system?
In most cases, yes — integrating with existing SCADA, MES, and other operational technology systems is a core part of most engagements in this space, rather than replacing them outright. The specifics depend on what the existing system exposes.
What happens when we add new device types or scale up the fleet?
The architecture is designed to accommodate device and protocol diversity and growing data volume from the start, so adding a new device type or scaling from a pilot to a full fleet is a planned extension rather than a rebuild.
Further reading
- Software Trends Shaping Climate Tech and Energy OperationsHow asset monitoring, SCADA/IoT integration, grid-edge coordination, and reporting automation are reshaping software needs in energy operations.
- A Buyer's Guide to Fleet Management SoftwareA fleet-specific buyer's guide covering core capabilities, hardware and connectivity, build-vs-buy, integration, and driver-data privacy.
- Software Platforms for Logistics Operations: What to BuildA practical guide to fleet telematics, warehouse management, route optimization, and shipment visibility, and how to sequence logistics software investment.
Considering a IoT & Connected Systems Software project?
Tell us what your devices or sensors currently capture, and what system that data needs to reach, and we'll assess feasibility before discussing scope.