We are building the AI engine for the real industrial world. By combining physical laws with deep learning models, we build high-fidelity industrial foundation models in weeks, requiring only dozens of samples—achieving what usually takes thousands of samples and years.
Or, Explore Our Technology →“Factories have lots of data”—this is the biggest misconception about Industrial AI. The reality is that data suitable for training high-quality models is expensive and scarce.
Vast streams of raw data do not equal usable training sets. The reality on the production floor is: data is unlabeled, misaligned in time, exceptions are uncleaned, and accuracy is unverified. After months spent cleaning this "raw data," the truly usable samples are scarce.
The more severe challenge is that industrial production processes are "alive." Just as you prepare to train a model, the process conditions may have changed, the equipment updated, or raw materials sourced from a new supplier. Each change can instantly render past data obsolete.
We don't just fit data; we learn the underlying physical laws. Our core technology—the ThinkMachine Model Engine—represents a new paradigm for industrial process predictive modeling.
Small Operating Samples
(Dozens of data points)
Physics-Informed Machine Learning
High-Fidelity Industrial Foundation Model
+ Key Process Parameters & Temporal Dynamics
The ThinkMachine engine acts as a System Identifier. It leverages state-of-the-art Physics-Informed ML to learn the unique fingerprints of your industrial process from every single operation.
By embedding physical laws or biochemical mechanisms as core constraints, our models achieve industry-leading accuracy with small data samples—you don't have to wait for years of data accumulation and governance.
The ThinkMachine model engine is an "explainable" model. It not only provides predictions but can also reveal the latent physical parameters driving change, enabling you to identify key factors impacting quality and efficiency.
Our ThinkMachine engine has been rigorously benchmarked against powerful baseline models (including classical Deep Learning like LSTM, RNN, Transformer, hybrid models like MLP-ODE, and latest operator learning architectures) on complex industrial data. The results demonstrate breakthrough improvements in generalization and predictive accuracy.
+700%
Our model achieved an R² score far surpassing other solutions on independent test sets. This represents over a 700% improvement in explaining the true process dynamics, marking a leap from failed models to highly predictive generalization on "unseen datasets."
-52%
Compared to the best hybrid models, the ThinkMachine engine reduced normalized prediction error by more than half. This makes high-fidelity, early-stage prediction of the entire process curve possible based on only the first 5-10% of the data.
Classic AI Models
ThinkMachine Model Engine
“In our recent metallurgy and biopharma production benchmarks, the ThinkMachine Model Engine reduced critical endpoint prediction error by over 20%, directly leading to significant quality improvements and operational cost savings.”
We provide ready-to-use foundation models for critical industrial sectors. These are not static software; they are digital twins that continuously learn and evolve with process and condition changes.
Optimizing chemical batch reactions, predicting metallurgical refining endpoints, controlling polymerization processes.
Predicting battery degradation and health status, forecasting the life cycle performance of energy systems.
Optimizing bioreactor fermentation yield, predicting cell culture growth, simulating pharmacological reactions.
Predicting material fatigue, simulating thermal dynamics, creating high-fidelity proxy models for complex simulations.
A high-fidelity AI foundation model is the key infrastructure for achieving a Digital Factory of operational excellence. We have successful practices accumulated across multiple scenarios with our partners.
Perform thousands of "virtual experiments" in the digital world to safely and efficiently find the optimal parameters for "golden batches," boosting yield and quality.
Precisely simulate and understand the impact of various operations on energy consumption and emissions, driving sustainable production and saving costs.
Identify subtle shifts in equipment health status from process data, enabling a move from reactive repair to proactive, early warning maintenance.
Build ultra-realistic virtual factories, allowing new operators to quickly grow into expert status in a zero-risk, repeatable simulation environment.
The complexity of the physical world shouldn't be a barrier to innovation. We are looking for pioneers to build the future of AI using First Principles. Whether you are a plant manager, process expert, or field engineer, contact us today to schedule an online session with a ThinkMachine expert. Let's start working on your industrial AI pain points.