The margin is already in your machine data.
We find the hidden patterns in the sensor streams you already collect, build a digital twin of your process, and re-tune it for less scrap and more capacity, with no new hardware and no production downtime.
Capabilities · Owl AI for Industry
Adaptive Process Control
We diagnose and re-tune the control logic already running your machine, turning over-cautious or mistuned behavior into measured, safe gains.
Proof: re-tuned an over-cautious feed control to model up to ~60% less sidewall wear, fully inside the machine’s hard force limit.
Digital Twins from Existing Data
A closed-loop simulator of your machine, its controller, and the outcome, built from the sensor history you already have. A safe place to test changes before they touch production.
Proof: a working twin built entirely from historical logs, with no new hardware and no downtime.
Explore the capability, with a live simulator →Predictive Scrap & Quality
We surface the hidden drivers of first-pass scrap and rework in your process data and turn them into early-warning signals and targeted fixes.
Proof: traced sidewall failures to sustained low-speed cutting, invisible in any single cut but clear across the full history.
Machine Health Monitoring
Continuous monitoring of force, vibration, and wear signatures to flag drift before it becomes downtime.
Existing Owl AI capability across robots, gearboxes, and rotating equipment.
“The controller was causing the defect it was trying to prevent.”
An over-cautious feed control was slowing the tool to protect each part, but the prolonged slow cutting was the very thing wearing it. Reading the machine’s own sensor history, we rebuilt the control logic and modeled up to ~60% less sidewall wear, fully inside the machine’s hard safety limit (in simulation).
Read the full case study →
Think your process is leaving margin on the floor?
Let’s look at your data. We’ll tell you what the sensors already know, and separate what’s proven from what’s projected.