Simplex creates on-demand vision datasets rendered from 3D scenes to train AI models. We can create data for any scenario, saving companies millions of hours they’d otherwise spend collecting and labeling real data.
Synthetic datasets for vision models
Title: Simplex: Synthetic datasets for vision models | Y Combinator

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### Synthetic datasets for vision models

Simplex creates on-demand vision datasets rendered from 3D scenes to train AI models. We can create data for any scenario, saving companies millions of hours they’d otherwise spend collecting and labeling real data.



Simplex

Founded:2024

Team Size:2

Location:San Francisco

### Active Founders

### Shreya Karpoor, Founder

Shreya is the co-founder and CEO of Simplex. She holds a BS and MEng in Electrical Engineering and Computer Science from MIT. She previously built software at Tesla and Viam and researched locomotion and dexterous robotic manipulation at MIT.



### Marco Nocito, Founder

Marco is the co-founder and CTO of Simplex. He holds a BS and MEng in Computer Science from MIT. He previously built machine learning models to generate synthetic data at Waymo and built data infrastructure tooling at Viam and Bloomberg.



### Company Launches

[### Simplex: on-demand photorealistic vision datasets](

**TL:DR;** Simplex creates photorealistic vision datasets rendered from 3D scenes for AI model training. Submit a request [on our website]( to receive high-quality data and labels.



**Data request for the above sample:** _“Generate images and labels of a home kitchen with household objects on a center table. I need a variety of household objects in a variety of lighting conditions. Our desired labels are semantic segmentation and depth maps.”_

Hi everyone, we’re [**Shreya**]( and [**Marco**]( two MIT grads building Simplex.

Collecting vision data for model training is time-consuming, costly, and often unsafe. Shreya spent over 200 hours physically operating a robot to collect image training data during her research at MIT. Marco worked on machine learning for synthetic data at Waymo to solve this exact problem.

We realized data scarcity wasn’t just an issue in robotics – it affects any company training vision models. When fine-tuning foundation models or building a new dataset from scratch, teams must curate existing data or label and collect data themselves.

We resolve the data scarcity problem by generating photorealistic ground truth labeled datasets for **any scenario**. We can generate **millions of varied images** **from 3D scenes** using our physics engine pipeline.

Here’s how you’d use Simplex:

1.  Fill out our data request form [here]( – it takes less than a minute.
2.  Give us feedback on a few sample image/label pairs that we generate. Repeat if necessary.
3.  Once you’re satisfied, download your complete dataset.

We support semantic segmentation, captions, simulated LiDAR, depth maps, and bounding boxes. You can generate large volumes of randomized scenes or provide a CAD/phone scan model for more specific scenes.

**Our Ask**
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*   If you or someone you know needs vision data, fill out our 30-second data request [form.]( We’re taking a limited number of early customers.
*   If you have a more complicated request or would otherwise like to contact us, email [shreya@simplex.sh](

**The Team**
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[**Shreya**]( Computer science (BS and MEng) at MIT, software engineer at Tesla and Viam. Built simulation pipelines for locomotion and dexterous manipulation research at MIT.   
[**Marco**]( Computer science (BS and MEng) at MIT, software engineer at Waymo, Bloomberg, and Viam. Built machine learning models to generate synthetic data at Waymo.

#### YC Sign Photo


