Manual inspection misses 15–40% of defects — depending on fatigue, lighting, and shift number. iFactory AI vision runs at line speed with 99%+ defect detection accuracy, replacing physical inspection stops entirely. The Vision Transformer + CNN ensemble runs on NVIDIA Jetson at the edge for sub-20ms inference, with H200 handling batch retraining. Plant copilot LLM auto-drafts the end-of-shift defect summary directly into your quality system. Get a quote and live accuracy demo — fixed-price proposal within 5 business days.
AI Vision Quality Control for Discrete
99%+ Accuracy at Line Speed
Vision Transformer + CNN ensemble. Sub-20ms inference on NVIDIA Jetson. Replaces physical inspection stops — permanently. Shipped to your plant, deployed by our engineers, owned by you. No cloud. No recurring fees.
There Are Two Accuracy Bars. Discrete Manufacturing Needs the Higher One.
Consumer-grade AI vision platforms target 95% accuracy — acceptable for photo apps, inadequate for a precision machined part. iFactory targets the industrial bar: 99.5%+ on trained defect classes, with object detection at 98.5% false-positive rejection. In a plant running 40,000 parts per shift, the gap between 95% and 99.5% is 1,800 escaped defects per day.
Every Inspection Stop Is a Hidden OEE Tax. Eliminate It.
Physical inspection stops — where production pauses while a human checks a part — are the most overlooked source of OEE loss in discrete plants. The average discrete manufacturer sits at 66.8% OEE. Replacing inspection stops with inline AI vision directly recovers Quality and Performance losses. Schedule a line audit — we map every inspection stop and calculate your specific OEE recovery before the quote.
Vision AI
How 99.5%+ Accuracy Is Achieved — and Maintained
Two model architectures working together — a Vision Transformer for spatial context and anomaly reasoning, a CNN for pixel-level defect classification — running on a two-tier NVIDIA compute stack. The Jetson handles real-time inline inference. The H200 runs batch retraining when new defect classes appear. Talk to our vision team about your specific defect types and part geometries.
New defect class trained and deployed in <48 hours with as few as 50 labeled images. Models retrain overnight on the H200 node — no external data sent anywhere.
End-of-Shift Defect Summary — Written by AI, Reviewed by You.
The plant copilot LLM aggregates every vision inspection event across the shift, runs a Pareto of defect types and stations, identifies process drift patterns, and drafts the full quality report. What used to take a quality engineer 45 minutes now takes 90 seconds. Your engineer reviews, edits, and approves.
4,847 parts inspected. 4,814 passed. 33 defects detected (0.68% DPR). Vision system confidence: 99.6% average across all frames.
Process drift detected: Edge burr count at St-04 rose from 3 (Shift A) to 19 (Shift B) — consistent with tool wear pattern on the deburring wheel. Recommend unscheduled PM inspection before Shift C. I've drafted a CAPA and a work order. Ready to push to SAP QM when you approve.
Without the LLM: same report took a quality engineer 45 minutes of manual aggregation from 4 different dashboards. Every word and every record stayed inside your plant network.
Ask our support team how the plant copilot integrates with your existing QMS and SAP QM configuration.
From PO to 99%+ Inline Accuracy — In 12 Weeks
iFactory ships a fully pre-configured AI server. Our engineers mount the cameras, configure the lighting rigs, calibrate the models to your specific parts, connect to your MES and ERP, and train your operators. You provide power and an internet uplink. Nothing else.
Remote walkthrough of part geometries, defect class list, camera positions, and MES/ERP version. Fixed-price proposal issued within 5 business days.
NVIDIA Jetson + H200 server assembled. Vision Transformer and CNN models pre-trained on your defect classes using sample images. Camera rigs spec'd and tested.
Crate ships. Engineers arrive. Camera mounting, lighting calibration, network switch, MES integration — fully installed and commissioning-tested.
AI vision live at line speed. Accuracy validated against your acceptance criteria. Operator training complete. You own the server, models, weights, and all inspection data — outright.
What Plants Ask Before Deploying Vision AI
Typically 50–200 labeled images per defect class for initial training. For new defect classes post-deployment, the H200 retraining pipeline can produce a live model in <48 hours. We help collect and label images during the on-site install.
No. The Jetson edge node delivers sub-20ms inference — faster than the camera exposure time on most lines. The vision system runs between stations without any production stop. Schedule a line speed check if you have specific cycle time constraints.
The operator registers the new part in the vision console, collects 50+ images of the new variant (including known defects if available), and submits for retraining. New model live on Jetson within 48 hours — no vendor involvement required.
No. The fully-loaded Jetson + H200 AI server is supplied and installed by iFactory. Camera hardware, lighting rigs, and mounting are scoped in the fixed-price quote. Talk to support to confirm camera compatibility with your existing line infrastructure.
Get a Fixed-Price Quote. Or Join the May 13 Webinar.
Send us your part list, defect class descriptions, camera count estimate, and MES system. We return a written proposal — hardware, vision models, on-site install, operator training, year-one support — within 5 business days.
