# What is Lodestar? ### **The company** Lodestar was founded in 2019 in Silicon Valley with a vision to make it possible for everyone to benefit from intelligent systems. In order to achieve that goal we are building tools that anyone can use to create computer vision systems and automate repetitive tasks. We work with companies that want to automate repetitive processes so that they can provide a safer and more interesting work environment for their employees, higher quality products for their clients and better value to their shareholders. ### **The product** Lodestar is a complete management suite for developing computer vision models from video data. Our game-changing technology can help create production models dramatically faster than traditional AI workflows. #### Video annotation Everything you need to label a high-quality video dataset. Easily upload and navigate through hours of video or millions of images, manage annotators, create object categories or add custom metadata to annotations. #### Dataset management Experiment directly on the dataset while your labelers are working in the annotation tool. Stop wasting time creating and keeping track of dataset copies and manually slicing and dicing your data every time you run an experiment – work on a single source of truth. #### AI model training Design datasets with one-click GPU assignment and an integrated data science platform powered by Jupyter notebooks. Filter annotations down to the pixel level, train different inference models and validate dataset quality. ### Let's get started The link below will bring you to our quick start guide, everything you need to know to go create a high quality dataset from video. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.lodestar.ai/main.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.