What challenges did Asure face before AI implementation?

Analyzing thousands of call transcripts manually took about 14 days. The existing approach did not scale well for identifying themes, root causes, and next steps. Slower insight generation delayed product and customer support improvements.

Source

Evidence: Low35/100

Asure builds post-call analytics with Amazon Bedrock and Amazon Q in QuickSight

Use case typeCustomer experience analyticsUpdated Jun 13, 2026

Asure, a workforce management and HR software company, needed a scalable way to analyze thousands of customer support call transcripts after calls. The team converted call audio to transcripts, generated metadata such as summary, root cause, next steps, and callback or resolution indicators with Amazon Bedrock, and used Amazon Comprehend for additional sentiment and entity signals. Amazon Q in QuickSight enabled natural-language analysis across aggregated call data and individual-call insights so analysts could query trends and issues without writing SQL.

Organization: Asure
Industry: Professional Services
Location: United States
Published: March 2025

Reported outcomes

Time: Approximately 14 days

Time & speed

Planned next steps

The source says the organization aims to achieve: Identified trends and pain points earlier.

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 14 days decrease

AWS Machine Learning BlogMar 20, 2025Blog postInferred claimLow evidence strength

Asure reduced analysis time from about 14 days to minutes or even seconds.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Asure
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Post-call analytics
2Contact center analytics
3Natural language BI

Amazon Transcribe converted call audio into transcripts.
Amazon Bedrock generated call metadata fields including summary, root cause, topic, next steps, and callback or resolution indicators.
Amazon Comprehend provided additional sentiment and entity processing.
AWS Step Functions orchestrated the workflow across the pipeline.
Amazon S3 and Amazon Athena stored and queried transcript-derived data.
Amazon Q in QuickSight enabled natural-language Q&A over call analytics for trend analysis and reporting.

Technologies

Amazon Bedrock Amazon Q in QuickSight Amazon Comprehend Amazon Transcribe AWS Step Functions Amazon S3 Amazon Athena Amazon CloudFront Amazon DynamoDB

Architecture

Call audio is transcribed with Amazon Transcribe. AWS Step Functions orchestrates downstream processing where Amazon Bedrock generates transcript metadata and Amazon Comprehend adds sentiment/entity analysis. The outputs are stored in Amazon S3 and queried in Amazon Athena, then surfaced in Amazon QuickSight with Amazon Q for natural-language analytics. The article also describes a human-in-the-loop evaluation UI and Bedrock-based evaluation metrics.

Sources & evidence1

Evidence: Low35/100Evidence strength