Normalized claim
Mortgage processing time: 16 weeks
reducing the process timeline from 16 weeks to 10 weeks
Appian uses Amazon Bedrock and Amazon Textract behind Appian AI skills to automate document extraction, summarization, PII handling, and process workflows for enterprise customers. Named examples in the article include mortgage processing, trade-email entity tagging for a financial services company, and legal contract review. The solution is delivered through Appian AI Process Platform and pre-built generative AI skills rather than a bespoke model training program.
Reported outcomes
Email extraction time: 4× lower
Time & speed
Catalog median for time & speed deployments: −50% across 295 reported metrics. Compare benchmarks →
Normalized claim
Mortgage processing time: 16 weeks
reducing the process timeline from 16 weeks to 10 weeks
Normalized claim
Mortgage processing time: 10 weeks
reducing the process timeline from 16 weeks to 10 weeks
Normalized claim
Mortgage extraction accuracy: 98.3%
achieving 98.33% accuracy
Normalized claim
Post-closing audit backlog: 45 days decrease
reduce their post-closing audit backlog from 45 days to 1 day
Normalized claim
Post-closing audit backlog: 1 days decrease
reduce their post-closing audit backlog from 45 days to 1 day
Normalized claim
Email extraction time: 4 x decrease
a four-fold reduction in extraction time
Normalized claim
Email extraction accuracy: 95%
achieved over 95% accuracy
No explicit deployment-stage evidence found.
Primary read
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Appian AI service sits between Appian AI Process Platform instances and AWS AI services. It uses Amazon Textract for extracting structured data from scanned documents and images and Amazon Bedrock with Anthropic Claude LLMs for tasks such as summarization, classification, PII handling, and other generative AI skills. The article describes pre-built prompt templates and Appian Cloud delivering the AI capabilities within a managed SaaS environment.
AI-generated summary. Verify important details with the linked sources before relying on this case.
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