PhysMind achieves state-of-the-art performance on industrial AI tasks through expert agents — not bigger models. The new paradigm for manufacturing intelligence is here.
* Industrial Agent Benchmark — to be published by May 2026
Scaling foundation models cannot solve industrial AI. The paradigm must shift — from pattern-matching at scale to principled execution by expert agents. This is a behavior pattern change, not a compute problem.
Train bigger models on more data. Hope industrial knowledge appears. It doesn't — and it can't.
Package industrial expertise as executable skills. Agents invoke domain knowledge at inference time — no retraining required.
Ask a frontier model: is 1.13 or 1.8 larger? Pattern-trained models frequently answer 1.13 — "more digits = larger number" overrides actual numerical reasoning. In an industrial calibration system, this failure mode means a miscalibrated machine, a failed deployment, or a safety incident. More training data gives you more patterns. It does not give you physical principles.
The obvious path — train larger models on more industrial data — is blocked. Not by compute. By the fundamental nature of industrial knowledge.
Large models abstract repeat patterns from data. Pattern learning works for language generation, where approximate output is acceptable. It does not work for industrial execution, where correctness depends on precision, physical laws, and mathematical rules. Scaling gives you better pattern matching. It cannot give you physical understanding.
Ask a frontier model: is 1.13 or 1.8 larger? Pattern-trained models often answer 1.13 — "more digits = larger number" dominates. In industrial calibration, this failure causes miscalibrated machines, failed deployments, and safety incidents. More data gives more patterns. Not principles.
Calibration parameters, PLC integration quirks, factory safety protocols, machine-specific thresholds — none of this appears in any public training corpus. Collecting it is years of work per customer, per domain. The data gap is structural.
Industrial expertise lives in physical procedures, hardware-specific configuration, proprietary business logic, and operational intuition. Text-based training data cannot capture the form in which this knowledge actually exists.
Fine-tuning a model on proprietary data per customer — then managing drift, retraining on updates, and maintaining per-customer model versions — is prohibitively expensive, slow, and creates IP liability. Not viable at scale.
We introduce the Industrial Agent Benchmark (IAB) — a set of real-world industrial AI tasks where private expert skills systematically outperform the best foundation models. These are not toy benchmarks — they are tasks running on production hardware at real customer sites.
| Task | Domain | Best Foundation Model | PhysMind (Expert Agent) | Delta |
|---|---|---|---|---|
| Defect Threshold Calibration | Vision · Manufacturing | [XX]% | [XX]% | +[XX]% |
| Vision Model Deployment | MLOps · Production Line | [XX] hrs | [XX] hrs | –[XX]× |
| PLC Integration Scripting | Robotics · Hardware | [XX]% | [XX]% | +[XX]% |
| Camera Calibration Pipeline | Vision · Robotics | [XX]% | [XX]% | +[XX]% |
| EV Battery Cell Inspection Setup | Vision · EV R&D | [XX]% | [XX]% | +[XX]% |
| Robot Vision Problem Diagnosis | Robotics · Integration | [XX]% | [XX]% | +[XX]% |
| Potato Defect Detection Adaptation | Vision · Agri-Industrial | [XX]% | [XX]% | +[XX]% |
DRAFT IAB benchmark numbers to be filled in before publication. Tasks are representative of real deployments at Geely R&D, China defect detection manufacturer, and Idaho robotics SI. The Industrial Agent Benchmark has not yet been publicly released.
PhysMind is built on PhysicalFlow — an open-source expert agent runtime where industrial expertise is packaged as executable skills: code that runs, calibration data that gets applied, domain instructions that guide the agent. Not documents. Not training data. Running systems.
When an agent needs to calibrate a defect detection threshold, it doesn't retrieve a PDF about threshold calibration. It invokes a skill package containing the actual Python calibration script, the customer's threshold table, and validated domain instructions — then runs it against the real machine.
Interface & domain instructions. Tells the agent when and how to use this skill.
Executable code. ML pipelines, calibration scripts, integration logic, validation.
Calibration tables, threshold configs, safety limits, reference models.
Validation scripts the agent runs to confirm results before deployment.
The Expert Agent Framework is open source. We're providing the manufacturing AI community with the runtime that makes expert agents possible — and building the skills library on top of it.
The open-source framework that enables any AI agent to invoke executable expert skills. Model-agnostic. Works with Claude, GPT-4, Qwen, or any LLM. Designed for industrial production environments.
Open format for packaging expert knowledge as executable artifacts
Automatic skill discovery, selection, and invocation — no routing code
Isolated skill namespaces per customer, shared library for generic patterns
Built-in validation scripts confirm outputs before production deployment
Three paying customers across manufacturing, EV R&D, and robotics system integration — on two continents. Revenue is not aspirational. Contracts are signed or systems are running.
Silicon Valley is saying SaaS will be replaced by agents. The next infrastructure layer isn't software for humans — it's software for agents. PhysMind is the skills layer industrial agents need to reason and execute. As more agents are deployed across manufacturing, the more indispensable our library becomes. More agents = more invocations = more revenue. Phase 1 builds the library; Phase 2 monetizes it at scale.
Deploy industrial AI for enterprise customers. Charge for delivery. The real output isn't the project — it's the validated skills library accumulating underneath it.
Sell PhysicalFlow Runtime access as managed infrastructure. Industrial agents trace, orchestrate, and invoke expert skills on demand — per invocation or subscription. Every new agent deployment is a new revenue source.
Enterprise teams build, optimize, and publish their own skills on the PhysMind platform. We sell the toolchain. Their domain expertise becomes monetizable IP on our marketplace.
Keplore is building the Scale AI of the agent era — but the data compounds with every invocation. Skills are not consumed at training. They are invoked at runtime, validated in production, and become more valuable with every use.
Claude, GPT-4, and Qwen are capable general reasoners. They fail at industrial tasks because the domain knowledge — calibration procedures, PLC quirks, factory safety protocols — has never been packaged in a form they can execute. Keplore solves exactly this: packaging expert knowledge as executable skills that any agent can invoke. The skills library is the asset. Every customer engagement adds to it.
Scale AI's training data is consumed once. A skill package is invoked every time an agent does a calibration, deploys a model, or integrates hardware. Usage validates skills. Validated skills are more valuable. The library appreciates with every invocation.
Industrial knowledge doesn't exist in public training data. A competitor starting today must do the same factory-floor work Keplore is already doing — with an empty library, against a team with production experience. The head start compounds.
Phase 1 funds Phase 2. Phase 2 has three levers: project delivery, Skill Execution SaaS (PhysicalFlow Runtime, metered per invocation), and Skill Building SaaS (enterprise tooling to create and monetize their own skills). Marginal cost approaches zero as the library scales.
Former Chief AI Scientist at IBM. Serial entrepreneur — previously scaled a team from 4 to 1,000+, reaching $22M ARR. B.S. Zhejiang University, M.S./Ph.D. Johns Hopkins University.
Tenured professor, UCLA — dual appointment in Computer Science and Electrical Engineering. Ph.D. Computer Science & Ph.D. Astronomy, Johns Hopkins University. 80+ publications at NeurIPS, ICML, and ICLR.
Former Applied Scientist at Amazon Robotics. Led development of Amazon's large-scale high-performance AI platform. M.S. EE & M.S. Computer Science, Johns Hopkins University.
Robot Builder & Researcher at Johns Hopkins University Computing & Robot Lab. 10+ years of hands-on manufacturing industry experience.
Former Senior Full-Stack Engineer at Apple HQ. Led frontend architecture design for large-scale LLM platforms.
Former AI & Big Data Project Lead at Stanford GSB. Drove Exec Ed revenue to 3x growth through AI-powered program initiatives.
Former Core AI Engineer at PhyscLab. Currently leading AI capability development across multiple computer vision inspection projects. M.S. Computer Science, George Washington University.
Former AI Engineer at Alibaba Group. M.S. Computer Science, Johns Hopkins University.
PhysMind is building the expert agent infrastructure for manufacturing AI. We're talking to investors who understand the industrial AI opportunity and the paradigm shift from model scaling to expert execution.