The Right Way to Build
Industrial AI Foundation Models

Fusing Physics and Process Data

We build AI engines for the physical world. By embedding physical laws into deep learning, we deliver high-fidelity models in weeks using dozens of samples—not the thousands of samples and years required by traditional AI.

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Why Large Language Models (LLMs) Fail in Industry

LLMs are statistical models. Industrial systems are bound by physical laws. Porting LLMs to the factory floor faces fundamental, insurmountable obstacles.

No Physical Constraints

LLMs can't guarantee compliance with physical laws. In safety-critical systems, a "plausible" suggestion that violates thermodynamics can be catastrophic. Industrial AI must be physically trustworthy.

Fails at Continuous Dynamics & Real-Time

Industry runs on continuous differential equations (PDEs/ODEs). LLMs, built for discrete tokens, cannot solve continuous physical fields, and their latency fails the millisecond demands of real-time control.

Our Industrial Foundation Model (IFM) Architecture

Our layered architecture, from L0 (First Principles) to L4 (Applications), is built around a reusable L2 (Equipment Surrogate Model) core.
This framework ensures all models are physically trustworthy, composable, and scalable.

5-Layer Industrial Foundation Model Reference Architecture, showing layers from L0 First Principles to L4 Industry Applications, with L2 Equipment Surrogate Models at the core

Our Core Solution:
Equipment Surrogate Models (ESM)

We don't just fit data. We build pre-trained, "gray-box" AI models with embedded physical constraints. Each ESM is a reusable, scalable model of a specific "unit operation" (e.g., a reactor or furnace).

Input

Few-Shot On-Site Samples

(Dozens of data samples)

ThinkMachine.work Engine

Simulation + Physics-Informed ML

Output

High-Fidelity ESM

+ Parameter Identification (Explainable)

Feature 1: Real-Time Simulation (ms-level)

ESMs learn the entire solution space of mechanistic models (like CFD), compressing hours of simulation into milliseconds. This enables real-time monitoring and Advanced Process Control (APC).

Feature 2: Parameter Identification (Explainable)

By embedding physical equations (PIML) into the loss function, ESMs can reverse-engineer unknown physical parameters (e.g., heat transfer coefficients) from sparse data. This provides explainable diagnostics of equipment health.

Feature 3: Few-Shot Fine-Tuning

ESMs are pre-trained on general physics. Deployment at a new site only requires dozens of data samples for calibration via transfer learning, slashing project cycles and eliminating the cost of building models from scratch.

Proven in Real-World Industrial Scenarios

Our Physics-AI engine has been rigorously benchmarked against classic deep learning and hybrid AI models on complex industrial data. The results show breakthrough progress in both generalization and accuracy.

+700%

R² (Accuracy) Improvement

On independent test sets, our ESMs achieve R² scores that dramatically outperform other models. This 700%+ improvement in explaining process dynamics turns failed models into highly predictive assets on unseen data.

-52%

NRMSE (Prediction Error) Reduction

Our ESMs cut the normalized prediction error by more than half versus the best hybrid models. This enables high-fidelity prediction of the entire process curve using only the first 5-10% of data.

Classic AI Models

ThinkMachine ESM

"In our recent benchmarks, the ThinkMachine engine reduced critical endpoint prediction errors by over 20%, directly leading to significant quality improvements and operational cost savings."

ThinkMachine Foundry:
Our Equipment Surrogate Model (ESM) Library

Scalable industrial AI is built on reusable "unit operation" models. Our Foundry is a library of first-principles ESMs, pre-trained on simulation data (like CFD), ready to be combined and fine-tuned for your specific process.

Fermenter-ESM

Based on reaction kinetics and mass transfer mechanisms, simulates fermentation, polymerization, and batch reactions. Serving biopharma, chemical materials, and food & beverage.

Furnace-ESM

Based on high-temperature heat/mass transfer and multiphase flow, predicts metallurgical refining endpoints and heat treatment processes. Serving specialty metals, advanced materials, and semiconductors.

Cyclone-ESM

Based on fluid dynamics (CFD) mechanisms, optimizes mineral classification and oil-water separation efficiency. Serving mining, chemical, and environmental industries.

More Models...

Our foundry is constantly expanding to cover more core unit operations. Stay tuned.

L4: High-Value Industrial Applications

We deploy our pre-built ESMs as high-value solutions, rapidly customized for your needs through few-shot fine-tuning.

Process Optimization

Run thousands of "virtual experiments" in a digital world to safely and efficiently find golden batch parameters, improving yield and quality.

Energy & Cost Reduction

Precisely understand and simulate the impact of different operations on energy consumption and emissions, driving sustainable production for a cost and environmental win-win.

Predictive Maintenance (PDM)

Gain insight into subtle changes in equipment health (parameter drift) from process data, making the leap from "breakdown repair" to "proactive alerts".

Operator Training Simulator (OTS)

Build high-fidelity, physically trustworthy virtual plants, allowing new operators to quickly become experts in a zero-risk, repeatable simulation environment.

Let's Build Physically Trustworthy Industrial AI

The complexity of the physical world is no longer a barrier. We are looking for pioneers to build the future of industrial AI on first principles. We invite industry partners, managers, and engineers to validate and expand this Physics-AI paradigm and build the future of smart manufacturing.

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The Right Way to BuildIndustrial AI Foundation Models