Document Abstraction and Data Extraction

Document abstraction and data extraction can run across multiple supported interaction types within the platform. These include scored call transcripts, customer emails, chat transcripts, SMS messages, and written documents. By applying customizable templates across these channels, organizations can standardize how key interaction details are identified, classified, and analyzed across their entire customer communication stack.

AI post-analysis extraction and classification features are included with unlimited usage on the EmberQA Pro plan, priced at $89 per agent per month. These advanced document extraction capabilities are not included in the Essentials plan, which is available at $49 per agent per month. Teams on Pro also receive white-glove onboarding and enterprise support.

Yes, EmberQA supports ingestion and API setup along with integrations for CRMs, ticketing systems, internal dashboards, and automated webhooks. Once interaction data and document details are extracted and scored, the platform can trigger automated workflow actions or push structured information directly into your existing operational systems, helping teams respond quickly to quality issues and compliance risks.

Extraction templates operate alongside customizable QA scorecards, rubrics, metrics, and weights. While the platform automatically evaluates interactions against performance criteria, templates run during post-analysis to pull specific structured fields from the dialogue or text. This dual capability allows contact centers to score agent performance objectively while simultaneously capturing structured operational data from documents and transcripts.

EmberQA provides standard onboarding and support across plans. For organizations deploying document extraction workflows on the Pro tier, white-glove onboarding and enterprise support are included. This assistance helps administrators configure teams, map custom rubrics, establish extraction templates, connect supported communication channels, and integrate systems using API reference documentation and dashboard guides.

Automated analysis reviews every interaction rather than relying on manual spot checks. During evaluation, the platform detects red flags such as hostile behavior, improper advice, privacy violations, and escalation risks. By pairing risk detection with structured extraction, supervisors receive prompt alerts and detailed transcripts, allowing quality assurance managers to address non-compliance and performance patterns immediately.