Archetype AI

Most predictive maintenance finds only failures you've already seen. What if you could discover emerging anomalies without labeled data and then keep adapting as conditions change? Join us on September 24 to see how Newton Agents scale across equipment types without rebuilding models. Save Your Seat!

Physical AI is moving into the factory. Join NTT DATA and Archetype AI for a live conversation on how AI reasoning over sensor data is catching issues earlier, verifying critical work, and keeping operations safe. Featuring lessons from our Hyster-Yale collaboration. Thursday, July 30, 9:00 AM PDT. Register at the link in the comments.

Upstream oil & gas is one of the hardest environments to deploy AI in. Thousands of distributed wells, sensor data in every format, constantly shifting conditions, and decisions where being wrong shows up as lost production, a safety incident, or environmental damage. The conventional response has been a model per asset, per failure mode, per site. Each one needs its own development, customization, and retraining. That approach never reaches fleet scale, which is why most operational sensor data still goes unused. Foundation models change the economics. Newton, our Physical AI foundation model, reads vibration, pressure, flow, current, and acoustics across drilling, production, and rotating equipment. It ports from one well to the next without a rebuild each time, so a use case proven on one asset extends to hundreds at a fraction of the engineering cost. On June 18, Archetype AI walks through what it actually takes to design, adapt, and operationalize Physical AI across upstream use cases — from drilling and production monitoring to pumps, compressors, and other critical equipment. The session remains practical: preparing industrial sensor data, choosing between zero-shot deployment and machine-specific adaptation, and configuring Physical Agents across well fleets. What you'll leave with: 🔹 A framework for deploying foundation models across upstream assets and workflows 🔹When to use zero-shot, few-shot, or fine-tuning 🔹Best practices for multimodal sensor inputs and agent configuration 🔹Lessons from scaling AI across equipment fleets

Newton is heading to NVIDIA GTC San Jose! Colin Capobianco (VP of Sales), Larry Geller (VP of Strategic Partnerships), Sisinio Baldis (Head of Solutions Architecture), and Brandon Barbello (COO) will be on the ground and ready to talk about Physical AI, live demos, and what it actually looks like when a foundation model understands the physical world. Whether you're working on predictive maintenance, industrial monitoring, or trying to make sense of sensor data at scale, this is the conversation worth having. See you in San Jose in March! Book a demo with our team 👇 #NVIDIAGTC #PhysicalAI

Learn how our foundation model, Newton, can cut downtime, unlock hidden value from your sensor data, and transform your operations. Download the white paper to see how Physical AI delivers measurable ROI in weeks, not years.

Humanity is full of unanswered questions. At Archetype AI, we're building a platform for Physical AI and help enterprises unlock the hidden patterns and behaviors in their physical operations. Meet the Archetype AI Platform, powered by Newton foundation model. Our platform enables you to build and deploy Physical AI applications to perceive and reason about the physical world in real time. From raw data to real impact: monitor equipment to prevent costly failures, verify safety compliance in real-time without, transform complex sensor streams into actionable insights - no specialized models required. Built for organizations with large portfolios of physical assets, the Archetype AI platform makes it simple to deploy Physical AI applications. Request early access today and discover how Physical AI agents can transform your operations.