Fuel your visual inspection with GenAI data

Truly automate visual inspection across a wide range of cases that were previously impossible, using GenAI data
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dataspan.ai hero picture illustrating Gen AI of the datasetdataspan.ai hero picture illustrating Gen AI of the dataset

Let GenAI continue from where your defect data stops

Boost the performance of any AI visual inspection model by automatically generating missing data using dataspan.ai's self-serve platform.
dataspan.ai comparison of GenAI accuracy comparing to previous models
dataspan.ai generate synthetic data illustration: the process of creating new conceptsdataspan.ai generate synthetic data illustration: the process of creating new conceptsdataspan.ai generate synthetic data illustration: the process of creating new concepts
dataspan.ai graph of accuracy comparison of previous models and fenAI data generationdataspan.ai graph of accuracy comparison of previous models and fenAI data generation
dataspan.ai graph of accuracy comparison of previous models and fenAI data generation

Because visual inspection is meant to be fully automated

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Up to 90%

reduction in error rate compared to other deep-learning methods.
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Years

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Days

dramatically shortening data acquisition times.
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Millions

in potential cost savings on recalls, machine downtime, and manual inspection.
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"With dataspan.ai, we reduced defect detection errors by 87–92% across our product lines. The solution has saved us over $1M annually while eliminating production downtimes."

Quality Assurance Manager
Fortune 500 Medical Devices Manufacturer
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Case studies

Leading train operator

Challenge

Only 50 fractured wheel images out of millions hindered model training, causing high false positives and derailment risks.

Results

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12% higher accuracy
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Millions in annual savings through enhanced predictive maintenance.

Solution

dataspan.ai generated 5,000 high-variance GenAI images in just a few days, optimizing automated visual inspection model accuracy under real-world conditions.
Dataspan.ai case study illustration: leading train operator.

Leading medical devices manufacturer

Challenge

Rare stent defects required manual inspection, leading to production delays and recall risks.

Results

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92% error reduction
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Savings in machine downtime.

Solution

dataspan.ai simulated rare defect scenarios, enabling robust automated inspections.
Dataspan.ai case study illustration: leading medical devices manufacturer

Fortune 500 battery manufacturer

Challenge

Manual CT scan inspections became costly and error-prone after a major battery recall.

Results

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Significant cost savings and uninterrupted production.

Solution

dataspan.ai augmented the CT scan datasets with GenAI data, improving model reliability and recall prevention.
Dataspan.ai case study illustration: fortune 500 battery manufacturer: battery defects generated

Fortune 500 PCB manufacturer

Challenge

High variability overwhelmed defect detection models, risking delays and costly manual checks.

Results

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87% error reduction

Solution

Dataspan.ai produced diverse GenAI defect data, boosting model performance.
Dataspan.ai case study illustration: fortune 500 PCB manufacturer: PCB defects generated

Automated visual inspection, now a reality.

Boost the performance of any AI visual inspection model by automatically generating missing data using dataspan.ai's self-serve platform.
Get started