Manufacturing
Unplanned downtime and line defects are two of the most expensive problems in manufacturing, and both are solvable with the right data foundation. We build predictive maintenance models, computer vision defect detection, and unified OT/IT data pipelines that keep production running and quality consistent.
Unplanned downtime reduction target
Forge Manufacturing downtime reduction, pilot lines
Defect detection, at line speed
WhatmakesAIhardhere
Unplanned downtime is expensive and hard to predict
Equipment failures rarely give clean warning signs through traditional threshold-based monitoring. By the time a vibration or temperature reading crosses an alarm threshold, the failure is often already underway, forcing reactive maintenance and costly production stops.
Quality defect detection at line speed is difficult for human inspectors
Visual inspection at high production line speeds is fatiguing and inconsistent between shifts and inspectors. Subtle defects, the kind that cause the most expensive downstream recalls, are exactly the ones most likely to get missed by the human eye at speed.
Sensor and OT data lives in silos, separate from IT systems
Operational technology data from PLCs and sensors often stays isolated from IT systems like ERP and MES, making it hard to correlate machine health signals with production schedules, quality outcomes, or maintenance history in one place.
Skilled maintenance labor is harder to find and retain
Experienced maintenance technicians are retiring faster than plants can train replacements, and the tacit knowledge of how a specific machine tends to fail often leaves with them. Newer technicians need better tools to close that experience gap.
Whatwebuild
Predictive maintenance models
We build models trained on sensor telemetry, vibration data, and maintenance history that predict equipment failure risk before it happens, giving maintenance teams a scheduling window instead of a reactive emergency call.
Computer vision defect detection
We build computer vision systems that inspect products at line speed using existing or newly added cameras, flagging defects consistently across every shift, without the fatigue and variability that affects human visual inspection.
MLOps for edge deployment
We set up MLOps infrastructure that deploys and monitors models directly on plant-floor edge hardware, so inspection and monitoring systems keep working reliably even with limited or intermittent connectivity back to central systems.
Unified OT/IT data pipelines
We design data pipelines that bring PLC, sensor, MES, and ERP data together into a single operational data layer, so maintenance, quality, and production planning teams are working from the same current information.
Maintenance copilots for technicians
We build copilots that give technicians instant access to equipment history, past failure patterns, and repair procedures for the specific machine in front of them, helping newer technicians perform closer to the level of a retiring expert.
Howwe'dapproachthis
Commonquestions
Vibration, temperature, and current draw sensors on critical equipment are the most common and useful inputs, but the exact requirements depend on the failure modes you're trying to predict. During discovery, we assess what sensors you already have and recommend targeted additions only where genuinely necessary.