# Blog - How to | TagoIO

> Insights, tutorials, and news from the TagoIO team.

Insights, tutorials, and news from the TagoIO team.

- [How to Use an AI Assistant to Query Live IoT Device Data](https://tago.io/blog/use-ai-assistant-query-live-iot-device-data.md): A step-by-step walkthrough for querying live IoT device data with an AI assistant: opening TagoAI where the question lives, asking with specifics, checking the basis of every number, and raising the permission only when you want it to act.
- [The Fastest Way to Add a New Device Type to an IoT Platform](https://tago.io/blog/fastest-way-add-new-device-type-iot-platform.md): The fastest path to adding a new device type to an IoT platform: check the connector library first, then decoder, template, and test, in under an hour for supported hardware and under a day for anything else.
- [How to Scale from a 10-Device IoT Pilot to 1,000 Devices](https://tago.io/blog/scale-iot-pilot-to-1000-devices.md): Scaling from a 10-device IoT pilot to 1,000 devices breaks manual onboarding, eyeball monitoring, and hand-tuned dashboards. The four systems to replace them: provisioning, templates, automated intelligence, and fleet health.
- [How to Turn IoT Data into a Demand Forecast](https://tago.io/blog/turn-iot-data-into-demand-forecast.md): IoT data measures real consumption, which makes it a strong base for demand forecasting. How to build one and act on it with TagoIO.
- [How to Forecast Tank and Silo Levels Before They Run Out](https://tago.io/blog/forecast-tank-silo-levels-before-they-run-out.md): Level sensors tell you what's left. Forecasting tells you when it runs out. How to predict tank and silo depletion and trigger refills with lead time on TagoIO.
- [From Reactive to Predictive: Building Forecast-Based Alerts for IoT](https://tago.io/blog/forecast-based-alerts-iot.md): Threshold alerts fire after the problem starts. Forecast-based alerts fire before. How to build predictive alerting on IoT data with TagoIO.
- [How to Forecast Energy Consumption with IoT Data](https://tago.io/blog/how-to-forecast-energy-consumption-iot.md): A practical approach to forecasting energy consumption from IoT meter data: the seasonality that dominates load, the drivers to add, and how to run it on TagoIO.
- [How to Run Machine Learning Models on Your IoT Data with TagoIO Analysis](https://tago.io/blog/run-machine-learning-iot-data-tagoio-analysis.md): A practical path to running machine learning on IoT data inside TagoIO Analysis: where to train, how to serve predictions, and how to keep models fresh.
- [How to Forecast IoT Sensor Data: A Practical Guide](https://tago.io/blog/how-to-forecast-iot-sensor-data.md): A practical progression for forecasting IoT sensor data, from moving averages to seasonal models to machine learning, and how to run it on TagoIO.
- [How to Detect Anomalies in IoT Sensor Data Using AI](https://tago.io/blog/detect-iot-anomalies-with-ai.md): The four shapes a sensor anomaly takes, spikes, drift, silence, and correlated failures, why fixed thresholds miss three of them, and how to build detection on TagoIO with statistical baselines, TagoAI, Analysis, and Actions.
- [How Teams Actually Manage 1,000+ Devices in the Field](https://tago.io/blog/how-to-manage-1000-iot-devices-field.md): Managing 1,000+ IoT devices in the field breaks the habits that worked for your pilot. Here is what changes, and how tag-based provisioning, templated dashboards, and fleet monitoring keep effort flat as you scale.
- [Which IoT Platform Is Best for Your First Deployment?](https://tago.io/blog/which-iot-platform-is-best-for-your-first-deployment.md): Your first IoT deployment is a learning exercise. Pick a platform for where you are going, not just where you start, using time-to-value, learning curve, migration risk, and support.

[llms.txt](https://tago.io/llms.txt)