About Advex AI

Advex AI develops Vision AI that generates synthetic data to enhance computer vision models, significantly reducing the time and cost associated with data collection. By creating thousands of labeled images from just a few real photos, the technology improves model accuracy and adaptability for diverse applications in logistics and manufacturing.

```xml <problem> Computer vision models require extensive labeled data for training, which is often time-consuming and expensive to collect, especially for edge cases or variations that are difficult to capture in real-world scenarios. The lack of sufficient data can lead to reduced model accuracy and adaptability, hindering the deployment of effective vision AI solutions. </problem> <solution> Advex AI offers a Vision AI platform that leverages generative AI to create synthetic data for training and enhancing computer vision models. By generating thousands of labeled images from a small set of real-world images, the platform significantly reduces the time and cost associated with data collection and labeling. The generated synthetic data addresses data gaps and improves model performance, enabling faster deployment and adaptation to new products, defects, and process changes. The platform identifies key gaps in existing datasets and automatically generates the necessary synthetic images to fill those gaps, resulting in higher accuracy and more robust computer vision models. </solution> <features> - Generates thousands of synthetic images from as few as 5-10 real images - Identifies and addresses data gaps in existing datasets to improve model accuracy - Supports rapid adaptation to new products, defects, and process changes through on-the-fly data generation - Provides labeled synthetic data, eliminating the need for manual labeling - Enables the creation of diverse datasets with variations in lighting, weather conditions, and object characteristics - Offers case studies demonstrating improved accuracy in logistics, manufacturing, and other applications </features> <target_audience> The primary target audience includes companies in logistics, manufacturing, agricultural robotics, and retail that utilize computer vision for tasks such as object detection, defect detection, and robotic guidance. </target_audience> ```

What does Advex AI do?

Where is Advex AI located?

Advex AI is based in San Francisco, United States.

When was Advex AI founded?

Advex AI was founded in 2023.

How much funding has Advex AI raised?

Advex AI has raised 3500000.

Who founded Advex AI?

Advex AI was founded by Pedro Pachuca.

Pedro Pachuca - Co-founder/CEO

Location

San Francisco, United States

Founded

2023

Funding

3500000

Employees

6 employees

Major Investors

Construct Capital

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Advex AI

Score: 100/100

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

advex.ai 1K+

Crunchbase

Founded 2023 – San Francisco, United States

Funding

Estimated Funding

$3.5M+

Major Investors

Construct Capital

Team (5+)

Benjamin Pikus

Founding Engineer

Pedro Pachuca

Co-founder/CEO

Company Description

Problem

Computer vision models require extensive labeled data for training, which is often time-consuming and expensive to collect, especially for edge cases or variations that are difficult to capture in real-world scenarios. The lack of sufficient data can lead to reduced model accuracy and adaptability, hindering the deployment of effective vision AI solutions.

Solution

Advex AI offers a Vision AI platform that leverages generative AI to create synthetic data for training and enhancing computer vision models. By generating thousands of labeled images from a small set of real-world images, the platform significantly reduces the time and cost associated with data collection and labeling. The generated synthetic data addresses data gaps and improves model performance, enabling faster deployment and adaptation to new products, defects, and process changes. The platform identifies key gaps in existing datasets and automatically generates the necessary synthetic images to fill those gaps, resulting in higher accuracy and more robust computer vision models.

Features

Generates thousands of synthetic images from as few as 5-10 real images

Identifies and addresses data gaps in existing datasets to improve model accuracy

Supports rapid adaptation to new products, defects, and process changes through on-the-fly data generation

Provides labeled synthetic data, eliminating the need for manual labeling

Enables the creation of diverse datasets with variations in lighting, weather conditions, and object characteristics

Offers case studies demonstrating improved accuracy in logistics, manufacturing, and other applications

Target Audience

The primary target audience includes companies in logistics, manufacturing, agricultural robotics, and retail that utilize computer vision for tasks such as object detection, defect detection, and robotic guidance.