Machine intelligence for the factory floor.

When the problem needs more than prompting: physics-informed features, models that learn a machine’s normal operating envelope from telemetry alone, digital-twin residuals, and the closed loop that turns them into corrections. The deep end, where wrapper shops decline to quote.

Last updated: 20 August 2026

What is machine intelligence for the factory floor?

Machine intelligence for the factory floor is a model of how a machine actually behaves — learned from controller telemetry, CAM programs, tooling and material — used to measure how accurately a plan survives contact with the machine, then to close the loop: plan, machine, observe, correct. Labels are not a prerequisite; the machine’s own envelope is the signal.

What this is not

It is not a chatbot on a shop-floor PC. It is not a generic predictive-maintenance SaaS dashboard. It is not, on current signed work, reinforcement learning. Wrapper shops usually decline to quote this class of problem. That is the point.

How the work typically proceeds

  1. Baseline. Measure how accurately the plan survives contact with the machine. If you cannot measure it, you cannot close a loop.
  2. Machine-environment model. Learn a behaviour signature from controller telemetry, CAM, tooling and material, without waiting for a labelled failure dataset.
  3. Residuals and health. Compare live behaviour to the expected envelope; report a health index and the engineering reason it moved.
  4. Closed loop. Feed corrections back into planning. The manufacturer’s team owns the model at handover.

Current engagement

Base42 is contracted with ShiaanX (2026) on AI-led automation for CNC precision manufacturing. The public description is one glance: accuracy baseline, then a machine-environment model. Statement-of-work internals stay off this site. See current work.

Questions

What is machine intelligence for the factory floor?

Models of how a machine actually behaves — from controller telemetry, CAM programs, tooling and material — used to measure plan-vs-machine error and close the loop: plan, machine, observe, correct.

Is Base42 doing reinforcement learning for manufacturing?

Not on the signed ShiaanX work. That engagement is unsupervised and physics-informed: machine-behaviour signatures, digital-twin residuals, anomaly detection. Reinforcement learning is not a public claim until an RL engagement is signed.

Will our engineers be able to run this without Base42?

Yes. Handover is the design: your team owns the model, the data paths and the docs. Continuity does not depend on keeping us in the loop.

Bring us the problem everyone else quoted around.

Thirty minutes with the founder. You describe the problem; we tell you straight whether it is buildable, what it takes, and whether we are the right people.