DeflectBuster

Consumers can struggle to find a human support route, while businesses often see chatbot completion metrics without knowing where users requested help, abandoned or chose another published channel. DeflectBuster separates two products with a strict data boundary. The consumer helper scans the page the user is viewing for publicly presented contact links and organizes them locally. It does not uncover private contacts, bypass authentication, defeat access controls or automate messages. The business snippet runs only on a consenting customer's own properties and records minimized events such as explicit human-request clicks, repeated fallback, abandonment candidate and public contact-link selection. It does not receive a consumer extension's cross-site history or raw prompts by default. 'Rage escape' and a frustration index are hypotheses, not user intent or satisfaction truth; users leave for many reasons. Site event, conversation state, handoff request, abandonment candidate, user feedback, support-owner disposition, routing change, service acknowledgement and outcome remain separate. The product can expose blind spots in support design. It cannot spy on consumers, prove frustration, guarantee deflection or create a legitimate data moat from undisclosed browsing.

A small or mid-sized digital support team operating its own chatbot and wanting opt-in evidence about handoff and abandonment friction.