Simplex provides production-grade web agents designed for browser automation, specifically targeting vertical AI companies. These agents reliably handle complex, multi-step workflows and edge cases across legacy systems and various portals, including medical, billing, and government platforms. The platform enables developers to build and run robust automations where API access is unavailable.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Training robust computer vision models requires large, accurately labeled datasets, which are often expensive and time-consuming to acquire, especially for niche applications or edge cases. Manually labeling real-world images is prone to human error and can be a bottleneck in the AI development pipeline.
Solution
Simplex offers a platform for generating synthetic, photorealistic vision datasets on demand, complete with pixel-perfect labels, simulated LiDAR point clouds, and accurate captions. By rendering data from customizable 3D scenes, Simplex eliminates the need for real-world data collection and manual annotation. Users submit their project requirements, receive sample image/label pairs for feedback, and then receive a fully labeled dataset tailored to their specific use case. This approach enables rapid iteration and efficient training of AI models, even for scenarios where real-world data is scarce or difficult to obtain.
Target Audience
Simplex targets AI/ML engineers and computer vision researchers who need high-quality, labeled training data for specific applications, particularly in robotics, autonomous vehicles, and industrial automation.
Features
- On-demand generation of photorealistic images from 3D scenes
- Pixel-perfect labels, including semantic segmentation, bounding boxes, depth maps, and simulated LiDAR
- Customizable scene parameters, such as object types, lighting conditions, and camera angles
- Support for various label types, including semantic segmentation, captions, simulated LiDAR, depth maps, and bounding boxes
- Iterative feedback loop with sample image/label pairs to ensure data quality and relevance