170+ 94,000+ 80+ countries The client wanted to flag high-risk patients before symptoms, but data gaps and label issues blocked accurate models Vitals, and EHR feeds used different formats, so teams struggled to trace the true source for each metric No shared rule to tag “positive” patients, which slowed model setup and review Positive cases formed only a small slice of the cohort, tilting early model results Impact Delivered Model fills every field the moment a to onboard new event types, from New agents issue bindable quotes in We inserted an AI extraction layer that drops clean data straight into core systems. Here are some of the features: An Azure function fires on each upload and routes files to processing Captures names, addresses, vehicle details, VINs, and coverage in seconds Pandas, Regex, and NumPy tidy fields and build a structured JSON record JSON flows to policy-admin and quoting tools through azure API management No hardware to maintain; scaling happens automatically
consumer health & pharma
brands
employees across the
globe
with active operations Business Case
Source Mapping
Cohort Labelling
Class Balance
80% less manual
entry time
file arrives90% fewer key-in
errors
weeks to days3x faster quoteturnaround
under ten minutes Our Solution
Event-driven ingest
Custom Form Recognizer model
Python post-processing
Secure API hand-off
Serverless design
Tech Stack
Azure Form Recognizer
Python
Azure Functions
NumPy
Pandas
Azure API Management
