From c3446ebde8d9554683642a12ab22190c655c1b45 Mon Sep 17 00:00:00 2001 From: Todd Herman <80002434+toherman-msft@users.noreply.github.com> Date: Mon, 5 May 2025 15:19:51 -0700 Subject: [PATCH 1/2] docs: readme updates --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c16c1be0..6b579c6c 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Content processing solution accelerator -This solution accelerator enables customers to programmatically extract data and apply schemas to unstructured documents across text-based and multi-modal content. During processing, extraction and data schema transformation - these steps are scored for accuracy to automate processing and identify as-needed human validation. This allows for improved accuracy and greater speed for data integration into downstream systems. +Extract data and apply schemas across your multi-modal content, with confidence scoring and user validation enabling greater speed of data ingestion. Process claims, invoices, contracts and other documents quickly and accurately by extracting information from unstructured content and mapping it to a structured format. This template supports text, images, tables and graphs. + +These capabilities can be applied to numerous use cases including: contract processing, claims processing, invoice processing, ID verification, and clinician-patient visit record summarization.
@@ -14,7 +16,7 @@ This solution accelerator enables customers to programmatically extract data and Solution overview -The solution leverages Azure AI Foundry, Azure AI Content Understanding, Azure OpenAI Service, Azure blob storage, and Azure Cosmos DB to transform large volumes of unstructured content through event-driven processing pipelines for integration into downstream applications and post-processing activities. +This accelerator leverages Azure AI Foundry, Azure AI Content Understanding, Azure OpenAI Service, Azure blob storage, and Cosmos DB to transform large volumes of unstructured content through event-driven processing pipelines for integration into downstream applications and post-processing activities. Processing, extraction and data schema transformation steps are scored for accuracy to automate processing and identify as-needed human validation. ### Solution architecture |![image](./docs/images/readme/solution-architecture.png)| From c825e58f461c7be9ccc54c149c5b76040817f077 Mon Sep 17 00:00:00 2001 From: Todd Herman <80002434+toherman-msft@users.noreply.github.com> Date: Tue, 6 May 2025 09:24:12 -0700 Subject: [PATCH 2/2] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6b579c6c..360946c6 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ These capabilities can be applied to numerous use cases including: contract proc Solution overview -This accelerator leverages Azure AI Foundry, Azure AI Content Understanding, Azure OpenAI Service, Azure blob storage, and Cosmos DB to transform large volumes of unstructured content through event-driven processing pipelines for integration into downstream applications and post-processing activities. Processing, extraction and data schema transformation steps are scored for accuracy to automate processing and identify as-needed human validation. +This accelerator leverages Azure AI Foundry, Azure AI Content Understanding Service, Azure OpenAI Service, Azure blob storage, Azure Cosmos DB, and Azure Container Apps to transform large volumes of unstructured content through event-driven processing pipelines for integration into downstream applications and post-processing activities. Processing, extraction and data schema transformation steps are scored for accuracy to automate processing and identify as-needed human validation. ### Solution architecture |![image](./docs/images/readme/solution-architecture.png)|