THINGBOOK.io Information

Thingbook builds DriftMind, a foundation model for forecasting and anomaly detection on fast data streams. Time-series foundation models made a compelling promise: forecasts out of the box, with no training cycles and no data science project. DriftMind keeps that promise and adds what the category left out: a bill you can defend. Consumed as a service, one million forecasting operations cost approximately $80 with DriftMind, against $275 to $300 with the leading alternatives. On infrastructure you already own, the gap widens: one billion forecasts require less than $15 of compute, while self-hosting transformer-based models costs tens to hundreds of times more for the same workload. And unlike static pretrained models, DriftMind continues learning from the incoming stream, so accuracy holds as your data drifts. The proposition is otherwise the same across the category: cold-start capability, no conventional training cycles, far less dependence on specialised data science expertise. The difference is the cost of reaching comparable accuracy. Built for telemetry, sensor, industrial and telecom KPI network data, DriftMind runs the same lightweight binary from air-gapped edge devices to cloud microservices, and is available as an ISV microservice on Cumulocity and as a native app for Bosch Rexroth ctrlX OS. Validate it with your own numbers: create a free account at thingbook.io, or drop a CSV into the X-Ray tool and see DriftMind forecasting within seconds.