IoT Solutions Company
Software for connected devices, built across the whole path from edge to application.
A device on its own doesn’t tell you much. The value shows up in the path after it connectivity, the protocol layer, the data and AI built on top, and the application someone actually uses. We work across that full path, as one team.
Our Decade long experience, validated in numbers
Enterprise Customers
OVERVIEW
The work usually spans more than
it first appears to.
Device connectivity, the software layer, the data and AI on top, and the operations that keep it alive are often handled by separate teams, on separate timelines, with separate assumptions about what the others are doing. We’ve found the work tends to go more smoothly when one team carries it end to end.
Trusted by Leading Enterprises
WHAT WE DELIVER
Edge & connectivity
This is the layer that decides what data is even worth collecting. We write the software that sits closest to your hardware, before anything reaches the cloud.
- Protocol-agnostic and hardware-agnostic edge gateway software
- Firmware-adjacent device logic and embedded integration
- Plug-and-play onboarding for new devices, with secure boot
- Edge compute for filtering, buffering, and local rules before cloud sync
- 10BASE-T1S single-pair Ethernet network integration for panel-level devices
Typical edge stack
- Gateway runtime (Linux/RTOS)
- Local protocol drivers
- Store-and-forward buffering
- Secure provisioning agent
- OTA update client
Protocols & data integration
This is usually where IoT projects get stuck. Multiple device vendors, multiple protocols, no shared format. We’ve built this layer enough times including on our own Edge Foundry platform to know where it breaks.
- BACnet, Modbus (RTU/TCP), OPC-UA, MQTT and CoAP integration
- Multi-vendor, multi-protocol data normalization into one digital backbone
- Real-time telemetry streaming and edge-to-cloud pipelines
- Time-series ingestion, aggregation, and historian design
- Protocol anomaly detection for noisy or unreliable field data
Typical edge stack
ResNet18 + KNN
Modbus RTU
Modbus TCP
OPC-UA
MQTT
CoAP
LoRaWAN
Zigbee
Software, AI & data
Once data is flowing reliably, the real question becomes what to build on top of it. This is where we spend the most time with customers figuring out what’s actually worth automating or predicting, versus what just needs a clear dashboard.
- Application and platform development on top of device data
- Data engineering: ingestion, time-series storage, aggregation
- Predictive maintenance, anomaly detection, and forecasting models
- Dashboards and customer-facing apps people actually check
- APIs and integrations into the systems you already run
What shapes this layer
Who reviews the data, how often, and what decision it’s meant to support. Whether a model is warranted or a well-set threshold does the job just as well. And what happens, operationally, when the incoming data is wrong or incomplete.
Software, AI & data
A connected system that works in the demo and breaks in production isn’t done. We build the operational discipline in alongside the product, not after it ships.
- CI/CD pipelines for edge, backend, and application code
- Over-the-air (OTA) update pipelines, tested before they're trusted
- Monitoring, logging, and alerting across every layer of the stack
- Security built in from provisioning to retirement secure boot, signed updates, zero-trust device auth
- Infrastructure that scales from a pilot site to a full fleet without re-architecture
Why this layer matters
It’s the difference between a pilot that scales cleanly and one that needs to be rebuilt, an update that rolls out safely across a fleet, and data issues that surface early rather than going unnoticed for months.
How we scope a project
What end-to-end means in practice.
When we say end-to-end IoT software, we mean it literally: the same team that wires up edge connectivity is also responsible for the data pipeline, the AI on top of it, the application people use, and the operations that keep it running. That’s the model we bring to every IoT engagement, regardless of industry.
What “end to end” covers
One team, across every layer
It’s common to split this work across a hardware vendor, an app agency, and a separate analytics team. In our experience, the seams between those teams are usually where a project loses the most time. Keeping it under one team edge software, protocol integration, data engineering, AI, the application layer, and the operations to run it tends to make the handoffs disappear rather than just shrink.
We’ve supported this kind of work across smart buildings, energy and grid operations, datacenters, and industrial environments.
- Edge device connectivity and protocol normalization
- Application and platform software development
- Data engineering, pipelines, and time-series infrastructure
- AI and analytics built on top of trustworthy telemetry
- DevOps, monitoring, and updates to keep it running long after launch
How we work
From your first device to your full fleet.
01.
Discover & map
Audit devices, protocols, and data sources on site.
02.
Architect
Design the edge-to-cloud architecture and protocol strategy.
03.
Build edge & cloud
Develop gateway software, pipelines, and the cloud platform in parallel.
04.
Pilot & validate
Connect a real site, validate accuracy, harden security.
05.
Scale the fleet
Roll out provisioning, OTA, and monitoring across every site.
Where this shows up
Industries we build connected software for.
Smart Buildings & PropTech
Energy & Utilities
Datacenters & Critical Infrastructure
Industrial & Manufacturing
Microgrid & Grid Solutions
EV Charging & Smart Home
Oil & Gas / Heavy Industry
Construction & Field Services
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
Connect With us
Thinking through a connected device or system?
Tell us about the devices, the protocols involved, and what you’re hoping to learn from the data.
We’ll think through the architecture with you on the first call.