AI Quality Control Software for Manufacturing

Manual inspection is the bottleneck your quality system is built around

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

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See our work

The problem

Sound familiar?

  • 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?

Short answer

RaftLabs builds AI quality control software for manufacturing operations, covering computer vision defect detection trained on your product images, anomaly classification that distinguishes defect types and severity, inline inspection integration with production lines, SPC data feeds from inspection results, rejection workflow automation, and quality analytics dashboards. AI visual inspection typically achieves 95-99% defect detection accuracy on trained defect categories, operates at production line speed without slowing throughput, and reduces false rejection rates compared to manual inspection. Most quality control AI projects deliver in 10-16 weeks at a fixed cost.

Key takeaways

  • TensorRT-optimised models on NVIDIA Jetson Orin process a typical inspection image in 15-40ms, supporting up to 1,500 units per minute at a single inspection point.
  • A minimum viable training dataset is 500-2,000 images per defect class captured under actual production lighting and camera conditions, plus 2-3 pass images per fail image.
  • Every rejection is captured with defect class, severity grade, confidence score, and a cropped image, building the archive used for both root-cause analysis and model retraining.
  • False positive rate is tracked alongside detection accuracy and calibrated against your quality cost model, since excessive false positives drive rework cost that offsets defect-escape savings.

Trusted by

Vodafone logo
Aldi logo
Nike logo
Microsoft logo
Heineken logo
Cisco logo
Calorgas logo
Energia Rewards logo

Quality control AI delivery, by the numbers

defect detection accuracy (trained categories)
95-99%
unit coverage vs statistical sampling
100%
AI systems built
20+
cost delivery
Fixed

Manual inspection is the bottleneck your quality system is built around

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.

Capabilities

What we build

  • 01
    Computer vision defect detection

    Detection models matched to your inspection task, trained on your own defect taxonomy with confidence-scored thresholds tuned to your quality cost model.

    Built with
    YOLOv8 · EfficientNet
  • 02
    Inline inspection integration

    GigE Vision camera and lighting design specified for your defect types, with TensorRT inference triggering the reject gate within the inter-unit gap.

    Built with
    Basler · NVIDIA Jetson
  • 03
    Defect classification and severity grading

    Defect-type and severity-grade classification beyond pass/fail, with every rejection archived with a cropped image for root-cause and retraining use.

  • 04
    SPC data integration

    Real-time defect data feeding P-charts, Cpk/Ppk metrics, and Western Electric rule-based drift detection, replacing the end-of-shift tally sheet.

    Built with
    InfinityQS · Minitab
  • 05
    Rejection workflow automation

    Automatic reject/rework/scrap routing with reason codes populated into your ERP's quality notifications, plus batch-hold workflows at threshold breach.

  • 06
    Quality analytics dashboard

    Floor-level real-time status and management-level cross-line trend and Pareto analysis, correlating defect rate with production parameters.

How we work

From scope to live inspection system

  1. Week 1
    01

    Inspection and defect-taxonomy scoping

    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.

  2. Weeks 2-5
    02

    Data collection and hardware design

    Camera, lighting, and inference hardware specified, with a structured image-collection phase for model training.

  3. Weeks 4-12
    03

    Model training and integration

    Detection models trained and validated, with SPC and QMS integration built in parallel.

  4. Final 2-3 weeks
    04

    Commissioning at full line speed

    Validated at production throughput with your quality team before go-live.

Why us

Why manufacturers choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your inspection line also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building computer vision platforms.

  • 04
    Designed to your line speed, not the other way around

    Throughput impact during inspection is treated as a design failure, not an acceptable tradeoff.

  • 05
    False positive rate tracked, not just detection rate

    Threshold tuning balances catching defects against unnecessary rework cost.

Have a quality control AI project?

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.

Talk about your quality control project

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Frequently asked questions

Trained 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 what you need. We'll tell you what it would take.

We scope AI for Quality Control in Manufacturing in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.

Go deeper

Browse our case studies

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