AI Parser for Documents
A simple Generative AI solution built to solve the problem of users submitting unstructured documents instead of filling out surveys. The system automatically extracts and maps key data from these files to survey questions, instantly generating an automated, auditable risk score. This transforms static files into actionable user intelligence.
The Vision
The vision was to eliminate assessment response friction and low user engagement by becoming the central intelligence layer for user documents. This goal shifted the customer burden from manually interpreting compliance files to instantly receiving automated, verifiable risk scores, making the platform indispensable for both compliance and risk monitoring.
The PM Architecture
The architecture is LLM-driven and modular:
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The Ingestion & Staging handled secure upload of various file types (PDF, DOCX) to a Bronze Layer (Raw Storage).
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The AI processing layer shapes the core where the LLM is used. Survey questions are provided as context to the model to guide extraction and mapping of answers. A Scoring Service then calculates the risk score based on the extracted answers.
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The audit & output layer stores the structured answers in a Gold Layer and, critically, saves the original text snippet and location (page number) used by the AI to ensure full auditability.
The PM Approach
For the AI Parser project,Iprioritized rapid model validation before scaling integration. The project began with an MVP focused exclusively on proving the AI's ability to accurately extract answers from a limited set of documents, which defined the necessary accuracy threshold. Subsequent phases rapidly integrated the core technology: building the robust ingestion pipeline, deploying the Scoring Service, and finally developing the Audit Trail UI for verifiable results. Throughout the process, a critical feedback loop was maintained with Data Science to continuously refine the LLM's performance based on corrected customer validations.
PM x Engineering x Data Science - Agile Development
This required constant, deep collaboration among all three roles:
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Product Management (PM): Defined the AI accuracy/precision targets and the final auditable deliverable. PM structured the user experience for the Audit Trail, ensuring the feature was legally defensible and valuable.
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Data Science (DS): Developed and fine-tuned the LLM prompting strategy to meet the PM's required accuracy targets and implemented the logic to map extracted data to the assessment schema.
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Engineering: Built the scalable ingestion pipeline and the Scoring Service. They partnered with DS to operationalize the LLM and built the integration layer connecting the AI's output directly to the Assessment and Audit Trail modules.
Result
The project delivered a foundational Generative AI Parser that eliminated a critical bottleneck. The solution automates the extraction and scoring from unstructured supplier documents, resulting in immediate, auditable risk scores for customers. This capability significantly increased survey completion rates and elevated the platform's value proposition to intelligent, automated risk verification.
Wireframes & Concepts
