Manufacturing-Grade Data for Computer Vision

Eliminate the data scarcity bottleneck with physically simulated defect datasets. Train assembly line robotics to detect real-world failures that occur once in a thousand units.

The Problem

Modern manufacturing faces a critical challenge with computer vision AI

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Class Imbalance

Production lines yield millions of perfect units but almost zero images of defects. Models trained on this imbalanced data fail in the real world.

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The Synthetic Gap

CGI and Unity simulations fail to accurately model how light refracts off scratches, chrome, glass, and clear plastic—physics matter.

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False Positives

Without diverse defect training data, models confuse harmless reflections with critical flaws, causing costly false positives and line stoppages.

Our Solution

Physically simulated defects captured with real sensors under controlled conditions

Ground Truth Data

We don't use CGI or 3D rendering. We physically stress real-world industrial components to create authentic defects, capturing them with high-fidelity sensors under varying lighting conditions.

Real Photons, Real Sensors

Not simulations—authentic light physics

4K Resolution

3840×2160 minimum for edge device conditions

100% Owned IP

Perpetual royalty-free commercial license

Production-Ready Labels

YOLO and COCO format ground truth bounding boxes

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Manufacturing-Grade Datasets

Photons hitting real sensors

Real-World Applications

How industry leaders solved critical computer vision challenges with manufacturing-grade datasets tailored to their specific use cases

EV Battery Welds

The Challenge

Electric vehicle batteries require thousands of tiny welds. Chrome-plated welds create intense "specular highlights" (white glare) that confuse AI models trained on synthetic data.

Distinguishing harmless light reflections from critical micro-scratches
Teaching AI to ignore reflections in high-reflectivity surfaces
False rejections costing millions in wasted production time

Our Custom Dataset

Custom dataset of high-reflectivity welds photographed under harsh lighting conditions to create "hard negatives" that teach AI to ignore reflections.

Deliverables: 1,000+ units | 6+ lighting conditions | 4 defect categories

Results

Models trained on real-world glare data achieve 95%+ accuracy on reflection scenarios

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Custom Manufacturing Datasets

We don't build one-size-fits-all datasets. Every project is engineered for your specific manufacturing process, materials, and quality standards.

Your Proprietary Use Case

We build datasets for your specific manufactured item and quality parameters

Stress testing tailored to your product specifications
Defect classification matching your QC requirements
Lighting and angle variations relevant to your assembly line
Custom defect injection methods for your material

Ready to build a dataset for your specific use case?

Technical Specifications

Enterprise-grade quality and compliance standards

Image Resolution

4K minimum (3840×2160) for production-line accuracy

Sensor Types

High-fidelity mirrorless and thermal sensors for edge devices

Lighting Setup

6500K diffused LED + directional spotlights for stress testing

Ground Truth Labels

YOLO and COCO format bounding boxes with pixel precision

Intellectual Property

100% owned. Perpetual royalty-free commercial license.

Privacy Compliance

Zero GDPR/privacy risks—inanimate objects only

Flexible Licensing

From proof of concept to full enterprise deployment

Tier 1

The Pilot Pack

Proof of Concept

$1,500

One-time license

  • 1,000 Images
  • 500 Perfect / 500 Defect
  • Single Object Type
  • Ground Truth Labels
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Tier 2

The Benchmark Suite

Model Training

$10,000

One-time license

  • 5,000 Images
  • 5 Distinct Object Types
  • Hard Negatives & Occlusions
  • Production-Ready Labels
Tier 3

Custom Lab

Enterprise Solutions

Custom Quote

Your proprietary components

  • Ship Your Parts
  • Custom Failure Modes
  • Exclusive Ownership
  • Data Deletion Guarantee

Ready to Eliminate Data Scarcity?

Test our Spark Plug dataset against your current model. See how ground truth data improves recall on hard-to-detect defects.

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