Optimize Production with Intelligent Manufacturing AI

Our AI platform integrates with virtually any industrial equipment and sensors including vibration sensors, temperature probes, pressure gauges, flow meters, energy monitors, PLCs, SCADA systems, and IoT devices. We support standard industrial protocols like OPC UA, Modbus, MQTT, and proprietary systems. If your equipment lacks sensors, we can recommend and install appropriate monitoring hardware. The AI works with both legacy machines from the 1980s and modern Industry 4.0 equipment.

Absolutely. We retrofit older equipment with affordable IoT sensors that monitor vibration, temperature, acoustics, and power consumption without modifying the machines themselves. The AI analyzes these signals to predict failures days or weeks in advance, even on equipment manufactured decades ago. Many manufacturers see the highest ROI from predictive maintenance on older, less reliable machines where unexpected downtime is most costly. The retrofit process is non-invasive and completed during regular maintenance windows.

Minimal to zero downtime. We conduct assessments and install sensors during scheduled maintenance periods or off-shifts. Data collection begins immediately without interrupting production, and AI models train in parallel with normal operations. Most deployments follow a phased approach starting with one production line or area, validating results, then scaling to other areas. The entire process is designed to minimize operational disruption while delivering quick wins that demonstrate value and build stakeholder confidence.

Computer vision AI inspects 100% of products at line speed with 99%+ accuracy, compared to human inspection that samples 5-10% with 80-90% accuracy due to fatigue and variability. The AI detects microscopic defects invisible to human eyes, maintains consistent standards 24/7, and eliminates subjective interpretation. It documents every inspection with images for quality records and root cause analysis. Rather than replacing inspectors, the AI handles repetitive inspection tasks while humans focus on complex quality issues and continuous improvement initiatives.

Implementation timelines vary by scope and complexity. Initial assessment and planning takes 2-3 weeks, followed by sensor installation and data collection over 4-6 weeks. AI model development and training requires 6-8 weeks using historical and live data. Pilot deployment on select equipment typically launches within 3 months, with full facility rollout over 6-9 months depending on factory size. You'll see initial insights and quick wins within 60 days, with measurable ROI emerging around month four as predictive models mature.