All backend configurations that power Pandora’s AI-driven data extraction were set up using an internal application called Pi Design Studio. This tool enables data engineers to define field-level logic for every data point that needs to be extracted or inferred such as diagnosis, lab values, or insurance details by leveraging RAG (Retrieval-Augmented Generation) and LLM (Large Language Model) pipelines.

Using Pi Design Studio, engineers can:

• Map each field to specific document sections (like lab reports or biopsy PDFs).

• Define how RAG retrieves relevant context for each field using embeddings and prompt templates.

• Set up LLM prompts to infer values based on retrieved context.

• Configure validation rules and acceptable field formats.

• Handle logic for multiple form types, so that the system can adapt dynamically to different specialty drug enrollment templates.

All of this logic is authored in structured Excel templates within Pi Design Studio, making the setup flexible and scalable.

Once these logic sets are authored, Precog — the automation layer — comes into play.

Precog hosts and manages fnFM bots like the Clinical Information Extractor and Tarpeyo Form Extractor. These bots execute the logic built in Pi Design Studio. On bot run:

• The clinical documents are pulled from source systems.

• Logic written in Pi is executed field-by-field.

• Outputs are pushed into Pandora for human-in-the-loop review and submission.