Training-Free Time-Series Intelligence.
Instantly.

Made for: _

Thingbook is the platform that powers DriftMind, an autonomous 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
Try API Now Try CSV X-Ray
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 DriftMind Edge close to your data. No dependencies, no cloud, no external connectivity required.

  • Offline Capable: Forecasts and detects anomalies without internet.
  • Single Docker Image: Everything you need in one container.
  • Same API: Identical endpoints and responses as the cloud — swap one URL and you're done.
  • Free Community Edition: 20,000 API calls included. Available now on Docker Hub.

Get it on Docker Hub

Two images available: thngbk/driftmind-edge (API only) and thngbk/driftmind-edge-lab (API + Jupyter notebook — explore instantly in your browser).

user@device:~$ docker run -p 8080:8080 thngbk/driftmind-edge
# API ready at http://localhost:8080
user@device:~$ curl -X POST http://localhost:8080/forecasters \
  -d '{"forecasterName":"sensor","features":["temp"],"inputSize":15,"outputSize":1}'
{"forecasterId": "e3a1...","forecasterName": "sensor"}

user@device:~$ curl http://localhost:8080/forecasters/e3a1.../predictions
{"anomalyScore": 0.02, "numberOfClusters": 4, "features": {...}}

# Or try the Lab edition — Jupyter included:
user@device:~$ docker run -p 8080:8080 -p 8888:8888 thngbk/driftmind-edge-lab
# Open http://localhost:8888 — demo notebook ready

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.

Work with us!

Join the team building the future of Edge intelligence. Upload your CV and tell us why you're a fit.