Document intelligence on AWS, integrated with an existing Azure estate
The organisation gained an AI capability without disruption to its core platform. The engagement produced a complete statement of work, cost model and target architecture for the production build.
Customer background
A US healthcare services organisation providing patient access, prior authorisation and pharmacy support services to manufacturers and providers. The organisation's core platform is hosted on Microsoft Azure and was not a candidate for migration.
Challenge
An Azure-native organisation required AI document processing for patient access workflows. A full platform migration was neither required nor desirable, but the AI capability needed to operate on AWS.
Solution
Rather than proposing a platform migration, Rabbit Cloud scoped a net-new document intelligence layer on AWS using Amazon Textract and Amazon Bedrock, connected to the existing estate over private connectivity, with Amazon Macie monitoring the data landing zone.
Solution architecture
The engagement was delivered on the Document Intelligence Pipeline reference architecture, adapted to the client's data, security and operating requirements.
Reference architecture
Results and benefits
The organisation gained an AI capability without disruption to its core platform. The engagement produced a complete statement of work, cost model and target architecture for the production build.
- AI document processing capability added without disruption to the existing Azure platform
- Private connectivity between the AWS document intelligence layer and the existing estate
- Amazon Macie monitoring on the data landing zone from the outset
- Complete statement of work, itemised cost model and target architecture delivered for the production build
AWS services used
About Rabbit Cloud
Rabbit Cloud is an AWS partner delivering cloud migration, platform engineering, managed operations, data platforms and AI/ML solutions for enterprises and software companies. Engagements are delivered by senior engineering teams to the AWS Well-Architected Framework, with security, compliance and cost governance addressed from the initial architecture.
Client identity withheld under a confidentiality agreement; identifying detail has been generalised.
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