Everysk is happy to announce the addition of two new large language model (LLM) robots to our ever-increasing library of digital robots. Together, the PDF Summarization Robot and Datastore Natural Language Query Robot further the capabilities of the company’s intelligent automation platform and allow hedge funds, asset managers, and other capital markets investors to analyze information and respond to market conditions on a timely basis.
Everysk PDF Summarization Robot
Everysk’s PDF Summarization Robot changes how investment managers interact with essential financial documents, including Form 10-Q filings and bank research reports. The robot employs advanced machine learning algorithms to distill lengthy documents into concise, actionable summaries. This efficiency saves countless hours that would otherwise be spent on manual document analysis.
Key financial metrics, market trends, and strategic insights become immediately accessible. With this robot, capital market managers quickly identify investment opportunities and risks. This capability enables more informed decision-making by highlighting relevant data points and narrative insights.
The technology’s adaptability to financial reports’ nuanced language ensures that summaries are accurate and tailored. The PDF Summarization Robot maintains the context and significance of complex market analyses, crucial for asset management professionals. In fast-paced financial environments, the precision and efficiency of generative AI for PDF summarization are invaluable, enhancing portfolio operations and strategic advantage by ensuring timely and clear information.
Pulling from one or multiple documents loaded via the File Retriever Robot, the PDF Summarization Robot uses a sophisticated map-reduce technique to map each document into an individual summary and then reduce the summaries into a final, holistic one that can be sent downstream in the flow.
Everysk Datastore Natural Language Query Robot
Everysk’s Datastore Natural Language (NL) Query Robot simplifies data analysis by enabling users to query financial datasets using natural language. This tool allows for quick extraction of insights from complex data, such as risk assessments and performance metrics, making it invaluable for financial professionals seeking concise, actionable information.
Raw data is within the Everysk platform as a “Datastore” element. Our standard datastores, powered by our proprietary risk and performance engines, are structured with properties or metrics in columns, while individual securities from a portfolio populate the rows. For example, a column labeled “SEC_VAR” may display the additive Value at Risk (VaR) for each security, providing a detailed financial risk assessment at the security level.
The introduction of the Datastore NL Query Robot revolutionizes how financial professionals interact with data. This innovative tool enables direct queries across multiple datastores, with the flexibility to target a different datastore each day. A workflow using this Robot could be configured like the below:
Once the workflow has been launched, a user inputs a prompt like, “Provide a plot with dates on the x-axis and the largest percent difference of VaR on the y-axis.”
In response, the robot analyzes two specified datastores based on their dates and generates a visual plot that highlights the security with the largest VaR percentage difference, such as stock CCRO3, in a clear and concise manner, and returns a result like the following:
By developing and deploying a series of specialized utility robots that utilize Large Language Model (LLM) techniques, Everysk caters to the specific needs of our clients on a selective basis. These robots perform efficiently at scale, leveraging the inherent parallelism in our automation processes. This strategic initiative represents a significant advancement in making sophisticated data analyses more accessible and actionable for our clients, enhancing their decision-making capabilities.
New robots on the Everysk platform are rolled out on a selected basis for clients with specific needs. Our robots perform at scale, using the parallelism inherent in our automations.
