Focaloid builds AI-first IoT and AIoT systems — from edge AI on the device to predictive analytics in the cloud to agentic workflows that act on the data. Sensors, gateways, platforms, and intelligence, delivered as one stack.
IoT without AI is no longer the enterprise default. The AI-in-IoT (AIoT) market reached $60.71 billion in 2025, and 84% of enterprises now identify AI as fundamental to their IoT strategy (Mordor Intelligence; industry research, 2025). The reason is simple: a sensor that streams data is a cost center; a sensor connected to a model that predicts failure, optimizes a process, or triggers an agent is a margin lever.
Focaloid builds for that second pattern. We partner with leadership teams to design and deploy AIoT systems where the edge device, the cloud platform, and the AI layer are engineered together — not bolted on after deployment. Our IoT and Edge AI capability sits inside Focaloid’s broader AI practice, which means the same teams building agentic AI and GenAI systems for our enterprise clients design the intelligence that runs on your connected devices.
The challenge has moved on from “how do we connect devices.” Today’s IoT problems are scale, intelligence, latency, and security — and they show up consistently across industries.
A single connected factory or hospital floor can stream millions of events per minute. Sending all of it to the cloud is slow, expensive, and often non-compliant. At least 40% of IoT-generated data is now processed at or near the edge. The architectural question isn’t whether to push intelligence to the edge — it’s what to push and where.
A predictive maintenance alert that arrives 30 seconds before a turbine fault is worth millions. The same alert delivered an hour later is a post-mortem. Enterprises need sub-second inference at the device, with longer-horizon ML training in the cloud.
Industrial sites run a mix of legacy PLCs, modern IIoT sensors, BLE wearables, cellular trackers, and cameras — each with its own protocol, vendor SDK, and data schema. Unifying them into one platform is non-trivial.
Every connected device is an attack surface. The IoT security market is forecast to reach $73.9B by 2026 (Allied Market Research) driven by ransomware, firmware exploits, and tightening regulation including the EU Cyber Resilience Act, IEC 62443, and NIST 8259A.
Most enterprise IoT projects die at the pilot stage — not because the technology fails, but because the system was never engineered for fleet-scale device management, OTA updates, model retraining, or operations.
End-to-end IoT platforms that ingest, normalize, and route data from connected devices at scale. We build on AWS IoT Core, Azure IoT Hub, Google Cloud IoT, IBM Watson IoT, and open frameworks like EdgeX Foundry and ThingsBoard — with custom platform builds when the off-the-shelf options don’t fit.
Edge AI is the difference between an IoT system that reacts and one that anticipates. We deploy optimized ML models onto NVIDIA Jetson, Arm Cortex / Ethos NPU, and other edge accelerators using TensorFlow Lite, ONNX Runtime, OpenVINO, AWS Greengrass, and Azure IoT Edge for sub-100ms inference, offline operation, and compliance with data-residency requirements.
AIoT is where Focaloid’s AI practice and IoT practice converge. We design connected systems where the AI layer is a first-class citizen, not an afterthought: predictive maintenance models trained on telemetry, computer vision QA on the production line, agentic workflows that close the loop between sensor signals and operational action.
Connected devices generate operational data that’s only valuable when it’s curated, modeled, and surfaced. We build IoT analytics platforms — streaming pipelines on Kafka, Kinesis, Confluent, time-series stores like InfluxDB and TimescaleDB, and analytical layers on Snowflake, Databricks, BigQuery — that turn raw telemetry into operational KPIs.
A digital twin is the live, AI-augmented mirror of a physical asset, line, or facility. The digital twin manufacturing market is projected at $47.24B in 2026 and it’s becoming the primary interface for IoT operational intelligence in industrial environments. We build twins that simulate, monitor, and optimize without touching the physical system.
Security is engineered into the device, the network, the platform, and the model. Our security work is aligned to IEC 62443 for industrial environments, NIST 8259A for device baseline, OWASP IoT Top 10, ISO/IEC 27001:2022 for organizational ISMS, and EU Cyber Resilience Act for products sold into the EU.
Many enterprises have first-generation IoT estates — proprietary protocols, on-prem-only platforms, dashboards without intelligence. We modernize these into AI-ready architectures without ripping out what already works: protocol bridges, edge gateways, cloud migration, and AI/ML layers added incrementally.
Predictive maintenance on rotating equipment, computer vision QA on production lines, OEE optimization across plants, AIoT-driven supply chain visibility integrated with OPC UA, MQTT, MES, and ERP layers.
Remote patient monitoring, connected medical devices (with HIPAA, HL7, FHIR alignment), ambient hospital intelligence, and edge AI on diagnostic devices.
Real-time asset tracking with 5G / NB-IoT / LoRaWAN, cold-chain monitoring, predictive ETA models, agentic exception handling — EDI, GS1, and IoT-telemetry ready.
Smart shelves, connected store analytics, computer vision footfall and shrinkage analytics, dynamic pricing tied to in-store IoT signals.
Vehicle telematics, predictive maintenance ML, ADAS support, connected dealer operations — engineered for OEMs, suppliers, and mobility platforms.
Smart grid monitoring, predictive analytics for transmission assets, distributed energy resource (DER) optimization, sustainability and emissions analytics.
In practice, almost every production AIoT system is hybrid: a smaller model at the edge for low-latency inference, a larger model in the cloud for training and complex reasoning, and an MLOps pipeline that keeps both in sync.
Most IoT shops bolt AI on as an afterthought. Focaloid’s IoT practice sits inside our AI practice — the teams building agentic AI, GenAI, and computer vision systems are the same teams designing the intelligence that runs on your devices.
That matters in IoT, where firmware quality, OTA pipelines, device lifecycle management, and SDLC discipline determine whether a system makes it from pilot to fleet scale.
We design the device, the gateway, the platform, the AI layer, and the agentic workflow that closes the loop — not as separate workstreams handed off between vendors.
ISO/IEC 27001:2022-aligned ISMS, IEC 62443 for industrial, NIST 8259A for device baseline, EU CRA-ready for products sold into Europe.
We engage with leadership teams from AIoT vision through production deployment and operations — not on a per-ticket basis.
Schedule a working session with our AIoT architects.
From connected device to agentic decision engineered as one stack.