# Our Features ## Dataset Management * **Data Integration**: Collect data from a wide range of sources, including embedded agent collectors, databases, cloud storage services like Google Drive and OneDrive. * **Data Cleaning and Transformation**: Utilize our proprietary AI model, "Astro," to automate the preprocessing and transformation of data into formats suitable for instruction-based LLMs. ## Efficient Model Training and Fine-Tuning * **Customizable Fine-Tuning**: Leverage your own hardware or use PropulsionAI's infrastructure for fine-tuning models to meet specific requirements. * **Instruction-Based LLM Preparation**: Convert data into formats such as question and answer formats, significantly reducing the time and effort needed for model training. ## Comprehensive Model Evaluation * **Orion Model**: Evaluate your trained LLMs against publicly available models like Claude and GPT-4, as well as previous versions of your own models, to obtain detailed performance reports. * **Performance Metrics**: Gain insights into model accuracy, efficiency, and other critical performance indicators. ## Seamless Deployment * **Flexible Deployment Options**: Deploy your models on PropulsionAI's service with an OpenAI-compatible REST API or on your own hardware. * **OpenAI-Compatible API**: Ensure compatibility and ease of integration with existing systems and workflows. ## Future-Proof Features * **Reinforcement Learning with Human Feedback (RLHF)**: Upcoming updates will include RLHF to enhance model training and performance. * **Function Calling and State Management**: Our platform supports function calling via engineered system prompts and state management using LLM State Machines. --- # 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.propulsionhq.com/copy-of-overview/our-features.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.