Designed and delivered services on Microsoft Azure to validate automated extraction of structured data from unstructured business documents, establishing a scalable foundation for intelligent document processing and downstream automation.
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The challenge
Critical business information was locked in unstructured documents, requiring manual processing that was time-consuming, error-prone, and difficult to scale. The organisation needed to validate whether AI-driven extraction could reliably identify a fixed set of predefined entities and integrate into existing cloud workflows.
The approach
Defined the target entities and document scope, then built an Azure-native extraction pipeline combining OCR, rules, and language models. Deployed and tested the end-to-end workflow within the client’s Azure environment, producing structured outputs and operational documentation.
The outcome
Successfully delivered and deployed a working environment that consistently extracted the required entities into structured outputs. Confirmed technical feasibility and architectural viability for scaling to production, while providing a clear roadmap and operational guidance to support future expansion of intelligent document processing and AI-driven automation use cases.
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