sf-service-ai-intake

Extract structured intake data from live call transcripts by using template-driven configurations to map transcript answers via the Models API directly onto a target Salesforce SObject. The runtime LWC listens to the call, applies non-destructive AI merges to empty fields, and generates auditable submission artifacts.

Populate Salesforce objects safely using non-destructive AI merges that only fill empty or non-edited fields, preserving agent input. This ensures CSR-facing workflows for service requests and claims maintain manual overrides while automatically capturing structured data from the live transcript.

Configure AI-assisted intake templates by modeling scenarios as data with specific questions, target SObjects, and visibility rules to drive a runtime LWC. This template-driven approach allows flexible submission paths, writing directly to a target object or saving JSON submissions for prototyping.

Live-call AI intake supports roadside assistance and claims processing by applying configurable templates to extract structured answers from live transcripts. It dispatches results to a target Salesforce SObject with safe agent overrides and auditable submission artifacts for end-to-end data capture.

Prototype Salesforce intake workflows by configuring the template to save JSON submissions instead of writing directly to a target SObject. This allows you to verify the end-to-end flow, test data extraction from transcripts, and validate intake logic before committing to production data.

Non-destructive AI merge for Service Cloud data capture is limited to populating only empty or non-edited fields, meaning it will not overwrite existing agent input. If a field already contains data, the AI extraction will bypass it, requiring manual agent updates to replace existing values.