AI Document Classification and Extraction Platform
Live
Problem
Providers were manually sorting and extracting data from high volumes of unstructured documents, creating processing delays and error risk.
Approach
Ran requirement gathering sessions with the client to define document types, classification rules, and extraction fields needed across manual and drive-based ingestion.
Outcome
Live and delivered to client.
Status
Live
Role
Led product execution end to end, defining manual and drive-based ingestion workflows and coordinating development of the classification and extraction engine.
Target Audience
Clinics, Medical Staff
Team Size
1 to 4 people
Project Duration
8 months
Key Skills
Data analysisuser journeysroadmappinguser acceptance testingrequirement analysis
Platform enabling healthcare providers to automatically classify and extract structured data from documents submitted manually or via drive based ingestion. Providers were manually sorting and extracting data from high volumes of unstructured documents, creating processing delays and error risk.
My Approach
Ran requirement gathering sessions with the client to define document types, classification rules, and extraction fields needed across manual and drive-based ingestion.
Key Challenges
Handling varied and inconsistent document formats accurately, given the range of unstructured inputs providers submit.
Designing a single workflow that supported both manual document submission and drive-based ingestion without adding complexity for end users.
Getting client validation and sign-off on classification accuracy before the platform could go live.
Live and delivered to client.