Models that learn from every
sensor you've ever shipped.
IoT-WorkS Telemetry AI ingests live sensor streams, learns per-asset baselines, and turns raw readings into anomalies, predicted failures and natural-language insights — explainable, auditable, hosted in the UK.
Four layers of intelligence on every data point.
Anomaly detection
Per-asset baselines learned online — isolation forest + LSTM ensemble flags drift, spikes and signature changes that fixed-threshold rules miss.
- ▸Streaming + batch detectors
- ▸Auto-baseline per asset, per shift, per season
- ▸False-positive feedback loop from operators
Predictive maintenance
Time-to-failure with calibrated confidence intervals. Models trained on your fleet's own history, not a generic vendor dataset.
- ▸Survival models (Weibull, Cox proportional hazards)
- ▸Confidence intervals, not just point estimates
- ▸Auto-creates a job in the Booking Engine when triggered
Natural-language queries
"Which reefers in Manchester ran above -18°C for more than 30 minutes last quarter?" — answered against a structured semantic layer, not freeform RAG over PDFs.
- ▸Semantic layer over MQTT + warehouse data
- ▸Audit trail — every answer cites the rows
- ▸Slack, Teams, portal, API access
Edge inference
Run quantised models on our gateways for sub-second latency and offline operation — sites with patchy 4G keep working, only summaries hit the cloud.
- ▸ONNX-Runtime + TFLite on ARMv8 gateways
- ▸OTA model updates with rollback
- ▸GDPR-friendly: raw data can stay on site
From the sensor to the decision — in five layers.
Built on open standards. No vendor lock-in. Edge to cloud, with the option to keep raw data on-site for GDPR-sensitive deployments.
Sensors
LoRa, NB-IoT, Cellular, BLE — temp, humidity, vibration, current, GPS.
Gateways
ARMv8 + ONNX runtime. Sub-second inference, offline-first.
MQTT + REST
TLS, mTLS-capable. Buffered forwarding when network drops.
Vize ML Engine
Online learning, drift monitoring, model registry, semantic layer.
Vize Portal
Dashboards, alerts, NL query, REST + webhooks for your stack.
Integrates with what you already run
connectors as of 2026.05Used in the field, every day.
Compressor failure, 7 days early
σ-shift on cycle frequency. Auto-creates a service job before product loss.
industrialBearing wear from current draw
Spectral signature of stator current — no extra vibration sensor needed.
healthcarePharmacy fridge audit-as-code
Per-fridge baseline, breach detection, MHRA-format export.
smart citiesAir-quality outliers
Detects sensor faults vs genuine pollution events with 92% precision.
energySub-meter anomaly clustering
Surfaces unusual loads against learned tenant patterns.
agricultureSoil-stress early warning
Multi-sensor fusion across moisture, temp, conductivity — 5–9 days lead.
Bring your own telemetry. We'll show you what's hiding in it.
Send 30 days of historical data — we'll run our anomaly + predictive engines and walk you through what we found.