Digital Engineering · IoT, Edge AI & Digital Twins
IoT & Digital Engineering Services
IoT development connects physical equipment, sensors and devices to software that monitors them, analyzes their data and acts on it. Aaga builds the connectivity, cloud platform, dashboards and edge AI with a lean, senior team, and works alongside your hardware and firmware engineers or device makers.
- Devices and machines connected securely over MQTT, OPC UA, Modbus or BLE gateways
- A scalable IoT back end for ingestion, device management and time-series storage
- Edge AI that runs vision and anomaly models next to the machine
- Dashboards, alerts, digital twins and predictive maintenance on top
- One line, site or device fleet to start
- Pilot first
- Connectivity, platform, data and AI in one team
- Edge to cloud
- Reply to your inquiry within one business day
- 1 day
What Do Our IoT Development Services Cover?
Everything from the device gateway to the decision: connectivity, platform, data, dashboards and AI.
Device Connectivity
MQTT brokers such as EMQX, Mosquitto or HiveMQ, plus gateways that bridge OPC UA, Modbus, BLE and LoRaWAN devices securely to the cloud.
IoT Platforms
Back ends on AWS IoT Core, Azure IoT Hub or open-source ThingsBoard: device registry, provisioning, OTA update orchestration and rules.
Edge AI
Vision and anomaly models optimized with ONNX Runtime, TensorRT or TensorFlow Lite on NVIDIA Jetson, industrial PCs or Raspberry Pi.
Dashboards & Alerts
Real-time dashboards in Grafana or a custom web app, on TimescaleDB or InfluxDB, with threshold and anomaly alerts to SMS, WhatsApp or email.
Digital Twins
Live software models of machines, lines or buildings that combine sensor data, asset data and state, for monitoring and what-if analysis.
Predictive Maintenance
Models on vibration, temperature, current and runtime data that flag likely failures early, so maintenance is planned rather than reactive.
From Connected Pilot to Fleet-Wide Rollout
Discovery
We review your devices, protocols, network constraints and the decisions the data should support.
Architecture
We design the edge, connectivity, platform and security model, sized for your pilot and your full fleet.
Connected Pilot
We connect one line, site or device group and deliver a working dashboard with alerts.
Add Intelligence
With real data flowing, we build anomaly detection, predictive models or a digital twin.
Scale
We automate provisioning, OTA update orchestration and monitoring, then roll out across sites.
A Lean IoT Team From Edge to Cloud
Large firms are well suited to global IoT programs that include custom hardware design and manufacturing. For software, data and AI on top of existing devices, a smaller senior team moves faster.
| Aaga | Typical large IT services model | |
|---|---|---|
| Starting point | One connected line or site with a live dashboard | Program-level strategy and platform selection |
| Team | Senior engineers covering edge, cloud, data and AI | Separate teams per layer, coordinated by managers |
| Platform choice | Cloud-native or open-source, chosen on fit and cost | Often aligned to partner platforms |
| AI | Edge and cloud models planned from the start | Often a later analytics phase |
| Best for | Manufacturers, operators and product companies piloting IoT | Global programs including custom hardware |
Comparison describes typical delivery models, not any specific company.
Who Handles the Hardware and Firmware?
Aaga focuses on the software, data and AI layers of IoT. For hardware, there are three common setups:
- Existing equipment. Machines and PLCs you already run. We connect them through gateways and industrial protocols such as OPC UA and Modbus, usually without touching the machine.
- Off-the-shelf sensors and gateways. We help select proven devices and configure them to publish over MQTT.
- Your own device. Your hardware team or device maker owns the electronics and firmware. We define the messaging contract, security and OTA update process with them, and build everything from the gateway up.
What Is IoT Development?
IoT (Internet of Things) development is the engineering work that connects physical things, such as machines, sensors, vehicles, meters and cameras, to software that collects their data, shows what is happening and acts on it. Digital engineering is the wider practice of building software products and platforms around physical operations, including digital twins and AI at the edge.
Aaga's IoT and digital engineering services cover the full software stack: connectivity, the IoT platform, data storage, dashboards, edge AI and predictive models. We work with your existing equipment and partner with your hardware team where custom devices are involved.
The Layers of an IoT System
Every IoT system has the same basic layers. Problems usually come from treating them separately.
1. Devices and gateways
Sensors, PLCs, controllers and cameras produce data. Many industrial machines speak OPC UA or Modbus rather than internet protocols, so an edge gateway translates their data and publishes it securely. Gateways also buffer data when the connection drops.
2. Connectivity
MQTT is the usual messaging backbone: devices publish to topics on a broker, and services subscribe to what they need. We design the topic structure, payload format (often JSON or Protobuf), quality-of-service levels and security model, with one certificate per device and access limited to its own topics. For remote sites we plan for cellular or LoRaWAN links and intermittent connectivity.
3. Platform
The IoT platform registers and provisions devices, routes messages, stores time-series data and orchestrates OTA updates. Depending on your cloud and budget, we build on AWS IoT Core, Azure IoT Hub or open-source tools such as ThingsBoard and EMQX. Our cloud solutions team handles the surrounding infrastructure.
4. Applications and AI
On top of the data sit dashboards, alerts, reports, digital twins and AI models. This is where the business value appears: less downtime, fewer quality defects, lower energy use, better asset utilization.
Managed cloud IoT or open source?
Managed services such as AWS IoT Core and Azure IoT Hub reduce operational work and integrate tightly with the rest of that cloud. Open-source stacks such as EMQX or Mosquitto with ThingsBoard give more control, avoid per-message pricing and can run on-premise, which matters for factories with strict network rules. Aaga recommends based on device count, message volume, data residency and who will run the platform.
Edge AI: Intelligence Next to the Machine
Some decisions can't wait for a round trip to the cloud. A camera checking parts on a conveyor, or a sensor detecting an abnormal vibration, needs a response in milliseconds. Edge AI runs optimized models on devices such as NVIDIA Jetson modules or industrial PCs, using runtimes like ONNX Runtime, TensorRT or TensorFlow Lite. The edge sends results and exceptions to the cloud rather than raw video, which saves bandwidth and keeps sensitive footage on site.
Our AI for robotics work uses the same vision and perception skills, and our machine learning team trains and retrains the models on your data.
Digital Twins and Predictive Maintenance
A digital twin combines live sensor data with asset information, such as model, configuration and maintenance history, into one model of a machine or line. Operators see current state at a glance, engineers can test changes in simulation, and models can predict what happens next.
Predictive maintenance is often the first use case. Models learn normal patterns in vibration, temperature, current and runtime, then flag drift that tends to come before a failure. Maintenance can then be scheduled during planned stops. Our AI for manufacturing page covers this and related quality-control use cases in more depth.
Instrument and Equipment Integration Experience
Capturing data reliably from equipment is a core skill for IoT. On our LIMS project, laboratory instruments were integrated with the lab management system for direct data capture, alongside sample tracking, workflow automation and audit trails. The same discipline, clear data contracts, validation and traceability, applies to sensors on a factory floor.
Security From Device to Dashboard
IoT expands the attack surface, so security is part of the design: unique device identities and certificates, TLS everywhere, signed firmware updates, least-privilege topic permissions, network segmentation between operational and IT networks, and audit logs. Our application and cloud security team reviews the design before rollout.
Who This Is For
- Manufacturers wanting visibility into machine uptime, quality and energy
- Equipment makers adding connected features and remote monitoring to their products
- Facility and utility operators monitoring distributed assets across sites
- Logistics and cold-chain businesses tracking location, temperature and condition
Start with a single line or site, prove the value with real data, then scale with confidence.
Frequently Asked Questions
IoT development services design and build systems that connect physical devices to software. They cover device connectivity, the cloud platform that ingests and manages data, dashboards and alerts, and analytics or AI on top. Aaga delivers the software, data and AI layers end to end.
MQTT is a lightweight publish-subscribe protocol designed for unreliable networks and constrained devices. It uses little bandwidth, supports different delivery guarantees and lets many devices and services share data through a broker, which makes it a common default for IoT messaging.
Edge AI means running AI models on or near the device, such as a camera, gateway or industrial PC, instead of in the cloud. It reduces latency and bandwidth, keeps working when connectivity drops and keeps sensitive images or data on site.
A digital twin is a live software model of a physical asset, line or facility, kept up to date with sensor data. Teams use it to monitor state in one view, test changes in simulation and run predictive models against current conditions.
Aaga focuses on connectivity, cloud platforms, data, dashboards and AI. We work with your existing equipment, recommend off-the-shelf sensors and gateways, or collaborate with your hardware team or device maker on custom devices.
Each device gets its own identity and certificate, traffic is encrypted with TLS, devices can only publish and subscribe to their own topics, and firmware updates are signed. On the platform side we apply least-privilege access, audit logging and network segmentation.
Start with one machine line, site or device group and a clear question, such as unplanned downtime or energy use. We connect it, deliver a live dashboard and alerts, then add predictive models once there is enough real data.
See Your Equipment in Real Time
Tell us about one line, site or device fleet. We'll propose a connected pilot with a live dashboard and alerts.
Scope My IoT Pilot