Cost of Goods Model extractor
Purpose
The purpose of Cost of Goods Model extractor is to extract necessary material information used in drug development, such as molecular weight, density, CAS Numbers, and other specified data, from public websites as well as datasheets and quotes on file for use in the COGS tool.
Primary users
The primary user is not specified in the provided information. The agent is associated with the LFS team and is intended to support users who need material data prepared for Cost of Goods Sold modeling in a drug development context.
Where it fits (process/stage/trigger)
This agent fits into the data preparation stage for the COGS tool, where material information must be gathered and structured before being entered into the COGS system. Initially, the generated outputs are used to manually update the COGS system, with a future objective of backend integration and human involvement only when data quality is low.
Key capabilities / workflow
The agent analyzes the required material information, extracts relevant data from public websites and available files such as datasheets and quotes, checks whether the extracted information is sufficient, and generates structured tables and JSON for downstream use. When data quality is not sufficient, the workflow loops back to extraction until the information can be prepared or flagged for review.
Inputs
Typical inputs are public websites, datasheets, and quotes on file that contain material data used in drug development. Specific input formats, datasets, and required source files were not specified in the provided information.
Outputs / Deliverables
The expected outputs are tables and JSON containing extracted material information for use in the COGS tool. These outputs are initially intended for manual updates to the COGS system, with possible future automated backend integration.
Value
Cost of Goods Model extractor reduces the effort required to collect and structure material data for COGS modeling, helping prepare consistent tables and JSON from available sources. Over time, it is expected to support greater automation while retaining human review when the system determines that data quality is low.
