Enterprise Time-Series Intelligence.
Instantly.
DriftMind is a self-adaptive forecasting, pattern discovery and anomaly detection engine that learns online from the first data point. Available as SaaS or deployable on-prem and at the edge, with zero training, zero GPUs, and near-zero latency.
Read the technical whitepaperWhy "Smart" AI Fails at Scale
The Central Brain Trap
GenAI and Deep Learning models are "Central Brains." They are smart, but slow and expensive. They require massive GPUs, cloud round-trips, and weeks of training history. They are overkill for operational data.
The Reflex Solution
Industrial systems need "Reflexes," not brains. Thingbook uses Online Pattern Clustering and Markov-inspired Temporal Transition Graphs to memorize shapes and detect anomalies instantly. It adapts to concept drift in milliseconds, not months.
Performance Benchmark: DriftMind vs. OneNet
We benchmarked DriftMind against OneNet (State-of-the-Art Deep Learning) on the standard ETTh2 dataset. DriftMind achieves comparable accuracy with orders of magnitude lower cost.
| Model | Hardware | Training | Inference Time (15k Series) |
|---|---|---|---|
| OneNet (Deep Learning) | High-End GPU | 25% Data Warm-up | ~58 Minutes |
| Thingbook DriftMind | Standard CPU | True Cold Start (0 Data) | ~25 Seconds (140x Faster) |
Federated Intelligence.
Run Anywhere.
Stop paying to stream healthy data to the cloud. Deploy our lightweight engine directly to your devices.
- Single JAR Binary: No Python envs. No dependency hell.
- Embedded Web Server: Includes Jetty + REST API out of the box.
- Offline Capable: Forecasts and detects anomalies without internet.
user@device:~$ curl -X POST http://localhost:8080/forecast -d @sensor_data.json
Built for Engineers
Dev Guide
Integration guides and performance tuning for high-throughput environments.
Swagger API
Test the cloud endpoints directly from your browser before deployment.
Python SDK
Integrate into your pipeline with `pip install driftmind-client`.
Architecture Deck
Deep dive into the Federated Edge design, TTG patterns, and benchmarks.