Enterprise Time-Series Intelligence.
Instantly.

Made for: _

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.

CPU-Only Architecture 140x Faster than Deep Learning Instant Adaptation to Drift
Read the technical whitepaper

Why "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:~$ java -jar thingbook-edge-engine.jar --port=8080
# Initializing DriftMind Kernel...
# Loading Behavioral Clusters... [OK]
# Starting Embedded Jetty Server on port 8080...
>> Listening for Time-Series Streams

user@device:~$ curl -X POST http://localhost:8080/forecast -d @sensor_data.json
{ "prediction": [24.5, 24.8, 25.1], "anomaly_score": 0.02, "drift_detected": false }