Abstract: Systems and methods for Large Language Models (LLMs) to generate an Artificial Intelligence (AI) business insight report using business insight data include obtaining business insight data for an organization where the business insight data is from a plurality of sources including from monitoring of a plurality of users associated with the organization; inputting the business insight data to a first Large Language Model (LLM) to generate an initial output for a business insight report; inputting the initial output to a second LLM for critiquing the initial output against a set of rules to check for predefined flaws and to check for what was done correctly to generate a critique; resolving the initial output and the critique to generate a final output; and providing the final output for the business insight report.

Abstract: A software as a service (SaaS) management platform, includes: an uploader process for receiving a plurality of contract files relating to purchases of a SaaS application; parsing logic that identifies terms in the plurality of contract files; normalization logic for assigning a normalization SKU to respective contract files based on the terms identified in the respective contract files; a background process that generates anonymized contract data by storing the terms of each contract file in association with the normalization SKU assigned to the contract file in an anonymous manner; a backend process that, responsive to a request from a client device, accesses the anonymized contract data to generate a distribution of terms of the anonymized contract data, generates benchmark data identifying an approximate location of terms of a given customer's contract file within the distribution, and returns the benchmark data to the client device for rendering through a user interface.

Abstract: A method implemented in a software as a service (SaaS) management platform, includes: receiving a plurality of contract files relating to purchases of a SaaS application; identifying terms in the plurality of contract files; assigning a normalization SKU to respective contract files based on the terms identified in the respective contract files; generating anonymized contract data by storing the terms of each contract file in association with the normalization SKU assigned to the contract file in an anonymous manner; responsive to a request from a client device, accessing the anonymized contract data to generate a distribution of terms of the anonymized contract data, generating benchmark data identifying an approximate location of terms of a given customer's contract file within the distribution, and returning the benchmark data to the client device for rendering through a user interface.