AI Quality Control Software for Manufacturing
Visual inspection by human operators is consistent at the start of a shift and inconsistent by hour eight. Some defect types, including surface micro-cracks and colour variation, are at the edge of human visual discrimination even under ideal conditions. Statistical sampling catches defects in aggregate but doesn't prevent defective units from shipping until the batch rejection threshold is crossed, by which point the root cause has been running for hours. AI visual inspection checks every unit at line speed, with consistent accuracy regardless of shift length, and feeds real-time defect data into your SPC system while the process can still be corrected.
Computer vision defect detection trained on your specific defect types and product variants
Inline inspection at production line speed with 100% coverage and no throughput impact
Defect classification by type and severity with confidence scores and image capture for every rejection
Real-time defect rate data feeding your SPC system for process control and root cause analysis
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Manual visual inspection missing defects that reach customers because inspectors fatigue over a 10-hour shift?
Sampling-based QC passing defective batches that inspection missed because you cannot check every unit?
Plain answer
RaftLabs builds AI quality control software for manufacturing: computer vision defect detection trained on your own defect classes, severity grading beyond pass or fail, inline integration with your production line and SPC system, and rejection workflows. We launch a validated first inspection point in 10-16 weeks at a fixed cost, then expand it to more lines and defect types as it proves out.
What to remember
Proof
The cost of poor quality runs an estimated 10 to 20 percent of sales for a typical manufacturer once scrap, rework, warranty, and recall are counted (ASQ, cost-of-quality research). Most of that cost is decided in the minutes between a defect starting and someone catching it.
Manual inspection is consistent at the start of a shift and drifts by hour eight. Sampling catches defects in aggregate, but a defective unit keeps shipping until the batch crosses its rejection threshold, hours after the root cause began. AI visual inspection checks every unit at line speed. Accuracy holds regardless of shift length, and real-time defect data reaches your SPC system while the process can still be corrected.
Capabilities
Detection models matched to your inspection task, trained on your own defect taxonomy with confidence-scored thresholds tuned to your quality cost model.
GigE Vision camera and lighting design specified for your defect types, with TensorRT inference triggering the reject gate within the inter-unit gap.
Defect-type and severity-grade classification beyond pass/fail, with every rejection archived with a cropped image for root-cause and retraining use.
Real-time defect data feeding P-charts, Cpk/Ppk metrics, and Western Electric rule-based drift detection, replacing the end-of-shift tally sheet.
Automatic reject/rework/scrap routing with reason codes populated into your ERP's quality notifications, plus batch-hold workflows at threshold breach.
Floor-level real-time status and management-level cross-line trend and Pareto analysis, correlating defect rate with production parameters.
How we work
We map your inspection line speed, current defect types, and defect escape cost. You leave week 1 with a written scope document and a fixed-price quote.
Camera, lighting, and inference hardware specified, with a structured image-collection phase for model training.
Detection models trained and validated, with SPC and QMS integration built in parallel.
Validated at production throughput with your quality team before go-live.
Every inspection model sits on one dial. Turn the confidence threshold up and you catch fewer real defects. Those false negatives are the units that reach a customer. Turn it down and you scrap or rework good parts. Those false positives burn margin on the line. The two failures cost different amounts, and the right setting depends on your numbers, not a vendor default.
We calibrate that threshold against your quality cost model. A safety-critical automotive part weights a missed defect far heavier than a false reject, so the dial moves toward over-flagging and a human reviews the borderline calls. A high-volume consumer good on thin margins cannot absorb the rework, so the balance shifts the other way. Either way we track false positive rate next to detection rate, because a model that hits 99 percent detection while wrongly rejecting good parts can cost more than the defects it catches.
We train models on your defect classes, not a generic library. Each class needs enough labelled examples to tell a real flaw from normal process variation, and the rare defects are the hard part: you might see one cracked weld in ten thousand units. For those we combine collected fails, targeted augmentation, and anomaly detection that flags anything outside the learned-good distribution. Inference runs at the edge on the line, so a network outage never stops inspection, and a human-review queue handles low-confidence calls and feeds every correction back into the next training round.
Why us
The engineers who assess your inspection line also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.
We scope the work, calculate the cost, and lock it in writing before any development starts.
Real engagements across SaaS, fintech, healthcare, and logistics, not every one published as a named case study. Our AI work spans computer-vision OCR, real-time edge inference, and image models in production.
Throughput impact during inspection is treated as a design failure, not an acceptable tradeoff.
Threshold tuning balances catching defects against unnecessary rework cost.
Proof
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Read moreTrained AI visual inspection systems typically achieve 95 to 99% detection rates on trained defect classes, compared to 80 to 95% for human inspectors over a sustained shift. AI accuracy doesn't degrade with fatigue or production rate, which matters most on three-shift operations running six days a week.
A minimum viable dataset typically requires 500-2,000 images per defect category captured under production conditions, plus pass images in a 2-3:1 ratio. Images are annotated using CVAT or Label Studio with a labelling standard your quality engineers define before annotation starts.
No. We design the inspection system around your line speed from the start. Camera selection, frame rate, resolution, and inference hardware are chosen so inspection throughput matches or exceeds line throughput, validated at full production speed during commissioning.
Yes. Inspection results are written to your QMS and ERP in real time. We integrate with SAP QM via OData or BAPI, Oracle Quality Cloud via REST, ETQ Reliance, MasterControl, and custom QMS platforms via database or file-based integration, with defect records carrying full production traceability.
Work with us
Tell us your inspection line speed, current defect types, and what your defect escape rate costs you. We will design the AI inspection system and give you a fixed cost.
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