The intelligence layer on top of every IoT signal.
IoT generates the data. AI turns it into decisions. TRACIO architects the applied-AI layer that sits on your sensor fleet — edge inference, anomaly detection, computer-vision sensor fusion, predictive maintenance, and LLM operations copilots —
built on PyTorch, TensorFlow, NVIDIA, Hailo, and Google Coral silicon, deployed against Azure ML, AWS SageMaker, and Edge Impulse.
Six places where AI changes the operational equation.
IoT without intelligence is just expensive plumbing. These are the layers TRACIO designs and ships.
Movement patterns & dwell drift
Unsupervised models that learn the normal cadence of an asset, person, or workflow — and surface deviations in real time. Catches lost equipment, stalled work, fraud, and process drift before they hit a KPI report.
Vibration, thermal, acoustic
Time-series models on IIoT sensor data predicting failure 7 to 90 days out. Integrated with the CMMS so work orders are raised before downtime, not after.
Sensor fusion
Vision models cross-referenced with UWB and BLE positioning for sub-centimetre identification of who is doing what, where — FOD prevention, hand hygiene attestation, PPE compliance.
On-device inference
Quantised, edge-deployed models on Hailo, NVIDIA Jetson, Google Coral, Edge Impulse, and STM32-class MCUs. Decisions made on the sensor — no round-trip latency, no cloud egress cost.
Natural-language & agentic
Retrieval-augmented assistants over your spatial event history, work-order logs, and SOPs. Operators ask "where was the gauge last calibrated?" and get an evidenced answer, not a search result.
What-if modelling
Real movement and process data feeding digital-twin simulations on NVIDIA Omniverse, Siemens Xcelerator, or Azure Digital Twins. Test layout, staffing, and process changes in silico before committing capex.
The stack we deploy on.
Frameworks
PyTorch, TensorFlow / Keras, ONNX, scikit-learn, XGBoost, Hugging Face Transformers, LangChain / LlamaIndex for RAG, Ray for distributed training.
Edge silicon
NVIDIA Jetson (Nano, Orin, AGX), Hailo-8 / Hailo-15, Google Coral TPU, Intel Movidius Myriad, Qualcomm QCS6490, STM32 MCUs with Cube.AI, Edge Impulse pipelines for Cortex-M.
Cloud & MLOps
Azure Machine Learning, AWS SageMaker, Vertex AI, Databricks, MLflow, Weights & Biases, Kubeflow, BentoML for serving, Modal / RunPod for burst compute.
LLM / Foundation models
Anthropic Claude, OpenAI GPT-4 family, Llama 3, Mistral, Gemini, Cohere — benchmarked against your latency, cost, and data-residency constraints.
Data & streaming
Kafka, Pulsar, Flink, Kinesis, Materialize, ksqlDB, Delta Lake, Iceberg — the spine that feeds real-time inference pipelines.
Digital twin
NVIDIA Omniverse, Siemens Xcelerator, Azure Digital Twins, AnyLogic, Unity Industrial Collection, Unreal Engine for visualisation.
Typical lift across our AI-on-IoT engagements.
Frequently asked questions.
What does applied AI mean for location data?
Practical models on top of your sensor and location streams - anomaly detection, predictive maintenance, dwell and flow analytics - not research projects.
Do we need our own data science team?
No. We bring the expertise and build models that run against the data you already collect.
Is our data kept private?
Yes. We work within your data governance and privacy requirements, and you retain ownership of your data.
Can AI run at the edge?
Yes, where latency or connectivity demands it - we design the right edge and cloud split for your use case.