AI Agents

Context

Built for banking within the Customer churn & retention stack, this agent is used after an issue is identified and an action is taken—when the bank must confirm that feedback, communications, and closure evidence are complete and recorded. Typical scenarios include complaint follow-ups, service recovery offers, retention callbacks, and journey tasks where proof of contact, confirmation, and closure is required for QA and audit.

What it does

The agent reviews the case record, outbound and inbound communications, and attached artefacts to verify closure requirements. It checks that the correct templates and disclosures were used, confirms acknowledgments (e.g., customer acceptance/refusal), validates that promised actions (fee reversals, limit changes, replacement cards) were executed, and ensures the case has the proper closure notes and timestamps. If something is missing or unclear, it generates a targeted prompt that states exactly which item is needed and where it belongs in the record.

Core AI functions

Document and message classification across email, letters, chat, and call notes; OCR and field extraction for dates, IDs, and acknowledgments; template/phrase matching to confirm approved wording; rule evaluation for required steps and evidence; cross-system checks to confirm promised actions occurred; and confidence scoring that routes only material gaps to review with precise, context-aware prompts.

Problem solved

Closure quality is often inconsistent—missing acknowledgments, unexecuted promises, or non-standard notes—leading to rework, repeat contacts, and weak evidencing in QA or audits.

Business impact

Cases close cleanly: retention tasks are fully evidenced, repeat work declines, customer experience improves, and QA/audit findings drop because every closure step is documented and traceable.

Integration and adjacent use cases

Integration is light–moderate: read the case from CRM/servicing, pull communications from email/chat/voice repositories and DMS, and write pass/flag outcomes plus prompts back to the same workflow—no core changes required.

Common combinations in this stack:

• Churn signal extractor (to prioritize who needs action),

• Root cause identifier (complaint & sentiment) (to ensure the resolution addresses the actual cause), and

• Churn retention exception handler (to route complex or policy-sensitive saves with full context and closure proof).