Artificial intelligence can help businesses make better use of their data, reduce repetitive work and create more responsive customer experiences. We help businesses in Dublin and across Ireland explore practical AI and machine learning opportunities, from defining the right use case to developing, integrating and improving a solution that fits the way the organisation works.
Real-Time Hardware & Cloud Telemetry
End-to-end IoT firmware, MQTT protocol brokers, BLE mesh networking, industrial gateways, and low-latency edge AI analytics.
Real-Time Sensor Telemetry
Hardware Reliability
Hardware SLA & Sensor Uptime
IoT Tech Stack
IoT Security & FOTA Audit
Connecting hardware devices to the cloud requires precision firmware, zero packet loss, and robust edge computing. We build scalable IoT ecosystems engineered for long-term field stability.
Scalable device fleet management across industrial, municipal, and commercial deployments.
High-availability MQTT data streams and continuous cloud ingestion pipelines.
Lightweight on-device AI inference providing instantaneous local action and offline protection.
Remote device diagnostics, OTA firmware releases, and automated anomaly alerts.
We bridge the gap between embedded hardware engineering, low-latency firmware, and enterprise cloud software.
#### 01\. Solve the right problem
People need to know what a system is producing and how to use that output. Clear workflows, appropriate review points and understandable reporting can make AI easier to evaluate and adopt.
An initial proof of concept may be useful, but long-term value depends on how a solution is maintained, improved and connected to the wider business. Planning for integration, monitoring and ownership helps avoid isolated experiments.
From prototype PCB designs and embedded firmware to enterprise cloud dashboards and predictive analytics.
We help identify where artificial intelligence could support your organisation and where it may not be the right fit. This can include reviewing processes, mapping opportunities, prioritising use cases and defining the information needed to assess potential value.
Machine learning can be used to identify patterns in suitable datasets and support tasks such as forecasting, classification or prioritisation. The project should begin with a clear question, appropriate data and a sensible way to evaluate whether the output is useful.
In supervised machine learning, a model learns from examples that include known outcomes. This approach can support use cases such as categorising enquiries, identifying likely outcomes or flagging items for review, provided the training data and evaluation process are appropriate.
AI can help businesses work with large volumes of written information, including documents, enquiries, internal knowledge and customer questions. Possible applications include search, summarisation, classification, extraction and carefully designed conversational experiences.
Some workflows involve repetitive steps that require information to be collected, checked, routed or prepared. AI-assisted automation can support these processes while retaining human review where accuracy, context or accountability matters.
A useful model or AI feature needs to work within the wider business environment. Integration may involve existing software, databases, reporting tools or customer platforms, alongside monitoring and review processes that help keep the solution relevant as requirements change.
A rigorous hardware-software engineering lifecycle from initial breadboard prototype to mass field deployment.
We begin by learning how the organisation operates, where information is created, which tasks create friction and what outcome would make the project worthwhile. This gives the technical work a clear business context.
We turn the initial idea into a specific use case with an intended user, input, output and decision or workflow. We also consider whether AI is the most suitable approach or whether a simpler solution would be more appropriate.
The available data, quality, permissions, structure and handling requirements are reviewed. This stage helps identify what can be built responsibly and what additional preparation may be needed before development.
We define how the system will operate, where it connects with existing processes and when people should review, approve or challenge its output. The aim is to create a workflow that is useful in practice, not just a technical demonstration.
The selected approach is developed and assessed against agreed criteria. Testing can cover output quality, edge cases, usability, reliability and the practical conditions in which the solution will be used.
Once the solution is ready for its intended environment, the focus shifts to adoption, monitoring and refinement. Feedback from users and real operating conditions can help guide future improvements.
Turn-key engineering deliverables providing clean documentation, version-controlled codebases, and cloud architectures.
We clarify the business problem, identify potential use cases, review the data landscape and agree what a useful outcome would look like. This stage helps determine the most sensible next step.
A selected concept is explored through a prototype, model or working feature. The focus is on learning quickly, testing assumptions and assessing whether the approach can support the intended workflow.
A proven solution is connected to the relevant systems and processes, then reviewed as people begin using it. Improvements can be prioritised around feedback, performance, governance and changing business needs.
Automated cloud release channel for building, signing, and broadcasting encrypted firmware binaries over the air.
Different sectors create different expectations, constraints and device environments. We tailor IoT hardware and software to operational realities.
AI can help teams organise documents, find information, classify enquiries, prepare summaries or support internal knowledge work. Human review remains important where professional judgement is required.
Retail businesses may explore product discovery, customer service support, demand analysis, content assistance or workflow automation. Any application should be designed around the customer journey and the quality of the available information.
Financial and insurance organisations may consider AI for document processing, operational support, risk analysis or customer communications. Projects in these areas require careful attention to data handling, oversight and applicable obligations.
AI may support administrative workflows, information management, research activities or operational analysis. Sensitive information, professional responsibility and appropriate governance must be considered from the outset.
Organisations in property and construction can explore uses such as document classification, project information search, maintenance workflows and operational reporting, depending on available data and system integration needs.
Public-facing and mission-led organisations may use AI to improve information access, route enquiries, support staff and make better use of limited resources. Accessibility, transparency and responsible human oversight are especially important considerations.
Connecting hardware to the cloud creates automated workflows, predictive maintenance alerts, and significant cost savings.
A defined use case keeps the project focused on a meaningful operational, customer or commercial requirement.
Feasibility and data assessment can help distinguish a realistic opportunity from an idea that needs more preparation or a different approach.
The solution can be designed around the actions people need to take, the information they need to see and the points where approval is required.
Explore practical articles on hardware security, wireless LoRaWAN protocols, and Edge AI deployment guidelines.
How to implement secure boot, X.509 certificate authentication, and secure element chips (ATECC608A) on ESP32 microcontrollers.
An in-depth technical comparison of network coverage, power consumption, data payload limits, and operating costs in Ireland.
Step-by-step guide to quantizing neural networks and running real-time object detection on low-cost ARM Cortex-M hardware.
Schedule a technical consultation with our senior IoT systems engineers in Ireland to plan your hardware architecture, firmware, and cloud platform.
IoT projects can begin with an early hardware idea, PCB redesign, firmware optimization, or fleet cloud ingestion scaling. The right starting point depends on your product roadmap.
You have a product idea or sensor specification. We help select microcontrollers, wireless modules, and build initial breadboard prototypes.
You have validated hardware schematics and need custom C/C++ firmware, MQTT brokers, and real-time telemetry control panels.
Your IoT hardware is in pilot testing. We audit low-power battery performance, signal stability, and cryptographic hardware security.
Scaling your device fleet to thousands of connected units with automated Over-The-Air firmware updates and 24/7 cloud monitoring.