Extract Data from PDF Forms with AI

EmberQA document extraction workflows are designed to process files, documents, and customer interaction records such as call transcripts, emails, and chat logs. Configured extraction templates run across these inputs to capture key structured fields, classify interaction types, and support ongoing quality assurance across contact center operations without requiring manual line-by-line review.

Extraction templates are configured to match specific data fields required by your quality assurance or operational rubrics. Once applied to analyzed documents or scored transcripts, the AI evaluates the text, extracts the defined information, and produces structured data. This structured output can then be referenced for metric scoring, operational monitoring, or integrated into broader reporting workflows.

AI post-analysis extraction, document data extraction workflows, and post-call tagging are included in the EmberQA Pro plan, which provides unlimited usage of included Pro features. Basic written interaction quality assurance scoring across chats and emails is also supported under the Essentials plan, allowing teams to choose the tier appropriate for their interaction review needs.

Yes. EmberQA provides an API reference and supports workflow integrations for the platform. This enables operations teams to pass structured interaction data and extracted document information into existing systems, CRM verification processes, and internal dashboards, ensuring quality metrics and captured form details flow directly into day-to-day business operations.

EmberQA offers multiple tiers of onboarding and support depending on your selected plan. Options range from standard support and basic onboarding to white-glove onboarding and enterprise support included with the Pro plan. Teams also have access to the dashboard user guide, API reference documentation, and direct email assistance at hello@emberqa.com.

Automated data extraction captures structured information consistently across interactions, eliminating the variability and time constraints of manual sampling. By automatically categorizing records and pulling critical details from forms, transcripts, and messages, quality assurance managers gain clear visibility into operational performance patterns and can deliver targeted coaching based on verified interaction data.