Scarce Expertise
The people who can specify a physical system correctly are few, and every project waits on their time.
Why Physical AI Stalls at the Design Stage
Physical AI has to work in the real world: on a mine site, a port, a rail network, a factory floor, an energy plant or an oil and gas facility. Designing a system for any of them depends on a small number of people who carry deep domain knowledge and deep technical design, and that expertise, not the technology, sets the pace at which physical industries can adopt AI and how far their output can scale. Most organizations close the gap with months of consulting before a single system is built. Praxis captures that expertise and puts it to work on demand.
The Design Delay
Reaching a credible reference architecture takes months, and the organization commits to it before it knows whether the approach will hold.
The Restart
Design knowledge stays with the people and firms who produced it, so the next project starts from the beginning again.
From Requirements to a Complete System
Engineers provide requirements or specifications, and Praxis autonomously resolves them into a complete, end-to-end design. It answers questions such as how to build a living operating system for a mine or an aeronautical facility, and the ontology beneath it, from plain specification documents.
Engineers provide requirements or specifications, and Praxis autonomously resolves them into a complete, end-to-end design. It answers questions such as how to build a living operating system for a mine or an aeronautical facility, and the ontology beneath it, from plain specification documents.
- Reference Architecture and System Design: The reference architecture and an end-to-end design of the system, with its functional architecture and specifications, built around how the operation actually runs.
- Ontology: The model of the industry and the operation beneath the system, ready to become the foundation of a Hyper Ontology deployment.
- Hardware and Models: Hardware selections and AI model choices matched to the environment, its constraints and its signals.
- Infrastructure and Deployment: The infrastructure patterns and deployment stack the system needs to run in production.
- Simulation: A live simulation of the solution that bridges operational understanding, so engineers can validate the design and operators and business leaders can see it working, add context from their own processes and refine it before anything is deployed.
Industrial Intelligence That Compounds
Four properties keep Praxis improving and the result in your hands.
Four properties keep Praxis improving and the result in your hands.
- Self-Improving: Praxis learns from published research, new knowledge work and its own design reasoning, so it designs better for a sector with every problem it resolves.
- Sector Depth: It carries the domain knowledge of physical industries such as oil and gas, mining, manufacturing, logistics, energy and transportation, where a wrong design has physical consequences.
- Engineers Decide: Praxis resolves requirements into a design. Your engineers evaluate it, challenge it and decide what gets built.
- Your Design Stays Yours: Praxis solves the problem for you and does not take your solution. The requirements, operational context and data you provide, and the design produced for you, belong to your organization.
Expertise Should Not Be the Bottleneck
Physical industries should not have to wait on the handful of people who can design their systems. Praxis puts that expertise in the hands of engineers, business owners and CIOs in hours, and each design leaves it better prepared for the next. It is the platform CodeNinja’s Forward Deployed Engineers use on Physical AI problems, and part of how CodeNinja delivers Adaptive Operations.