BypassRadar
An aggregate observability layer for regulated support teams that detects evidence of human-seeking paths, failed handoffs and repeat contact, then applies counsel-owned impact categories rather than claiming regulatory exposure.
The supplied research confirms two public bypass tools, widespread press coverage and a large regulated AI-support market, while finding no commercial bypass-specific observability product. That gap is real, but a bypass is not automatically failure: customers may prefer a human, use an accessibility path or follow a valid escalation. BypassRadar should measure observable journeys and handoff quality, avoid person-level profiling, and keep any regulatory weighting as a versioned counsel-owned policy—not a regulator-approved index.
The customer-operations, compliance, digital-service, risk, or AI-governance leader at a regulated company operating automated customer support.
Telemetry normalization and aggregate reporting scale, though journey mapping and policy review vary by tenant.
The product must expose harmful automation without treating every request for a person as evidence that AI failed.
Bypass evidence is newly visible, but no recently removed data or regulatory barrier is proven.
Four cross-references, five inbound links and independent public bypass evidence support an unoccupied observability wedge.
Intent is difficult to infer, regulatory weighting is jurisdiction-specific and adjacent quality products can add bypass metrics.
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