In LORS, users can ingest collections of documents, preprocess texts into embeddings, and store them in a vector database. When a query or summarization task is issued, LORS performs semantic retrieval to identify the most relevant text segments. It then feeds these segments into a large language model to produce concise, context-aware summaries. The modular design allows swapping embedding models, adjusting retrieval thresholds, and customizing prompt templates. LORS supports multi-document summarization, interactive query refinement, and batching for high-volume workloads, making it ideal for academic literature reviews, corporate reporting, or any scenario requiring rapid insight extraction from massive text corpora.

Charts Not Chapters revolutionizes how users interact with their data by providing a seamless experience in generating visually appealing charts and graphs. Utilizing advanced AI algorithms, it allows users to input raw data and receive instantly rendered charts tailored to their preferences. The platform supports numerous chart types and customizable options, making it suitable for anyone needing clear and impactful visual data representations, whether for business reports or academic projects.

We Report Work is a tool designed to simplify and automate the process of work reporting. By integrating with various work reporting tools like Gmail, Google Drive, and Calendar, We Report Work compiles data and generates concise work summaries in seconds. It allows users to refine the data and customize summaries according to their templates, making reporting accurate and time-efficient. This tool is ideal for employees, managers, and entrepreneurs who aim to enhance their work reporting process.