Captured from the live deployment, not a mockup
01 · The problem
Clinical text cannot be handed to a hosted model as-is, so every identifier is masked before the request leaves the service, enforced across a 38-endpoint API rather than at one choke point.
02 · How it works
- 01
Benchmarked four Amazon Bedrock models on latency and multilingual quality and selected Nova Pro at 605 ms against alternatives above 1,050 ms, setting maxTokens on every call to avoid quota-reservation throttling.
- 02
Matched prescriptions to Jan Aushadhi generics with substitution-safety warnings, surfacing savings up to 93.7%, and simplified clinical reports into 15 Indian languages.
- 03
Masked every identifier before it reaches the model across a 38-endpoint API, so a hosted model never sees patient-identifying text.
03 · What it cost, and what it returned
Benchmarked four Bedrock models on latency and multilingual quality to select Nova Pro at 605 ms against alternatives above 1,050 ms, and set maxTokens explicitly to avoid quota-reservation throttling.