> For the complete documentation index, see [llms.txt](https://docs.propulsionhq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.propulsionhq.com/copy-of-overview/our-features.md).

# 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.