Abstract: Methods and systems are described herein for a system that enables individual users or entities to assess high-level concepts expressed in natural language by identifying quantitative evaluation criteria for evaluating the concept. For example, a query evaluation system is provided herein that receives a user's query including natural language, e.g., indicative of a higher-level concept or idea to be deployed. The system may identify quantitative evaluation criteria for evaluating the concept, perform back-testing (e.g., to see how a particular strategy would have performed in the past) and allow the user to create a specific investment portfolio that tracks the original intent of the user. The concept may be tested, and its performance evaluated before deploying it to a user portfolio.

Abstract: A system and method generate answers to user queries by providing natural language responses containing direct citations to primary sources. The system comprises a data collection pipeline that ingests, processes, and organizes data from multiple sources, and a retrieval mechanism that processes user queries, identifies relevant data, and employs a machine learning model, such as a Large Language Model (LLM), to generate natural language responses based on the retrieved data. The generated responses are augmented with direct references to the primary sources, ensuring accurate attribution and up-to-date information. This system combines the natural language capabilities of LLMs with the direct connections to primary sources provided by traditional search engines, delivering real-time, dynamic processing of resources without incurring high re-training costs.

Abstract: A system and method generate answers to user queries by providing natural language responses containing direct citations to primary sources. The system comprises a data collection pipeline that ingests, processes, and organizes data from multiple sources, and a retrieval mechanism that processes user queries, identifies relevant data, and employs a machine learning model, such as a Large Language Model (LLM), to generate natural language responses based on the retrieved data. The generated responses are augmented with direct references to the primary sources, ensuring accurate attribution and up-to-date information. This system combines the natural language capabilities of LLMs with the direct connections to primary sources provided by traditional search engines, delivering real-time, dynamic processing of resources without incurring high re-training costs.