AI for IoT and location data: only after the data feed is reliable.
AI only pays when identity and location events underneath are trustworthy. We design the feed first, models second, on stacks you can own (PyTorch/TensorFlow, edge silicon, Azure ML / SageMaker), not a black-box platform of ours.
Six places AI changes the operating equation, after the feed is trustworthy.
IoT without intelligence is expensive plumbing. These are the layers we design on top of a clean locating feed. We don't sell hardware, so our advice stays independent.
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 build on: yours to own.
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.
What an applied-AI engagement delivers.
What you get
- A data-readiness audit of the feed: identity, location and sensor quality
- A use-case shortlist ranked by value and by how ready the data is
- Model and edge/cloud architecture on stacks you own
- A pilot with a measured baseline and written pass/fail criteria
- A handover so your team can run, retrain and monitor the models
How it runs
- Feed first: data readiness is checked in a discovery & scoping stage (typically 1 to 5 days on site)
- Pilot design & oversight: with timings agreed per site
- Build is scoped per project, with a gate after each stage
- You own the models, code and data. Nothing runs on a platform of ours
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.
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