Training Data Documentation

This document provides information regarding the datasets used by Thinking Machines Lab to develop its generative artificial intelligence systems and services, including its models (collectively “AI services”). This information reflects general practices, not specific to any single model.

1. Dataset Sources

Thinking Machines Lab’s AI services are developed using a variety of data sources, including publicly available data, data provided by partners via access agreements, and internally generated data, including synthetic data.

2. Intended Purpose

Thinking Machines Lab uses the collected data to develop its AI services, which are designed to understand and generate content, which may include text, images, audio, and video. The datasets are selected and curated to enable the AI services to develop broad capabilities, which may include language understanding, reasoning, visual comprehension, and audio and video processing.

3. Data Types and Amount

Thinking Machines Lab’s AI services are developed using datasets containing trillions of tokens. The training data consists of a broad variety of content types, including text, images, audio, and video, drawn from publicly available sources or acquired from third-parties. Thinking Machines Lab also uses synthetically generated data to supplement its training datasets.

4. Inclusion of Public Domain or IP-protected Data

The datasets Thinking Machines Lab uses to develop its AI services includes content that is in the public domain as well as content that may be subject to intellectual property protection.

5. Data Acquisition

Thinking Machines Lab’s services were developed using publicly available content obtained from the open internet and publicly accessible data repositories. Certain datasets were also obtained from third parties.

6. Inclusion of Personal Information or Aggregate Consumer Information

Thinking Machines Lab’s training datasets may contain personal information or aggregate personal information, as defined under applicable laws, such as names or other details that individuals share publicly on the internet or with Thinking Machines Labs.

7. Cleaning, Processing, and Other Modification to Datasets

Thinking Machines Lab takes steps to clean, process, and modify datasets used for its AI services. These processing steps, which vary by data type, may include deduplication and filtering to remove junk or other low-quality data, and are intended to improve the data’s usefulness for model training and support the responsible development of the AI services.

8. Data Collection and Use Timeline

Thinking Machines Lab collects and uses datasets to train and improve its AI services on an ongoing basis. Thinking Machines Lab began collecting and using datasets for model development in 2025.

9. Use of Synthetic Data

Thinking Machines Lab generates and uses synthetic data in the development of its AI services.

Model Summary: Inkling