Our technology

Where diffusion models meet industrial complexities.
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Let GenAI succeed where other synthetic data methods fail

dataspan.ai leverages advanced diffusion models to generate photo-realistic synthetic defect data, succeeding where traditional methods like anomaly detection and few-shot learning fail by minimizing false positives and enhancing data diversity.

Human in the loop

Interactive feedback with subject matter experts ensures synthetic data accurately reflects critical defect scenarios and nuances.
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Self-similarities

dataspan.ai harnesses recurring patterns in industrial data to create realistic, context-aware synthetic defect datasets.
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We’ve bridged the gap between academic research and real world scenarios, so you don’t have to.

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What to inpaint?

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We implement advanced techniques like DreamBooth, ControlNet, Universal Guidance, and LoRAControlNet in industrial conditions. These methods help us identify critical defect types—such as scratches, cracks, or missing parts—and inpaint them onto non-defective images. This ensures the defects are both realistic and contextually accurate for industrial training purposes.
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Where to inpaint?

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By implementing advanced research techniques like SegGPT, we analyze the component's functional and aesthetic areas. This allows us to select plausible locations for defects while avoiding regions where defects would be unrealistic. As a result, defects are placed meaningfully and contextually in background images.
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How to manipulate existing elements in the data?

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Alongside inpainting, we apply advanced techniques to modify existing elements within images. These manipulations simulate scenarios that accurately represent defective conditions, ensuring realistic and comprehensive training data.
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Which pixels to inpaint? What is the shape?

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Leveraging proprietary research, we generate defects with precise pixel-level accuracy, carefully considering their shape and size. This ensures synthetic defects closely mimic the natural irregularities found in industrial environments.
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How to deal with high-res photos?

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We employ advanced techniques to handle high-resolution data commonly found in industrial production lines, ensuring that computer vision synthetic data seamlessly matches the original resolution and maintains detail accuracy.
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How to eliminate AI hallucinations?

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We implement advanced filtering techniques, including DreamSim, ARNIQA, TOPIQ, and IP-Adapter, in both supervised and unsupervised modes. As part of this approach, dataspan.ai leverages SME-guided validation to remove irrelevant data, such as unrealistic artifacts or AI-generated hallucinations.

The dataspan.ai’s differentiation

Self-serve platform

Eliminate the need for costly professional services, while empowering even non-AI experts to optimize computer vision models with ease.

Unmatched data quality

Achieve optimal accuracy with GenAI data that closely resembles real-world scenarios, covering both common and edge cases.

Strong research team

Our team of computer vision researchers applies the most advanced research to practical use cases in the industrial space.

Purpose-built

our solutions specialized in industrial visual inspection models for detecting high-value and complex defects.

Unique feedback mechanism

Let the 'human in the loop' refine data with ease, leveraging an intuitive, interactive LLM interface.

Get Started with diffusion models synthetic data

Leverage the power of diffusion models to bridge the data gap and achieve highly accurate synthetic data for visual inspection. Start transforming your processes today with dataspan.ai's data-efficient computer vision platform.
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