White label

Multi-tenancy is a software architecture where one application instance serves multiple customer organizations, called tenants. Logical controls keep each tenant’s data separate even though the underlying infrastructure is shared. Isolation models vary. Some platforms use a shared database with tenant ID columns distinguishing each customer’s records. Others use separate schemas per tenant. The strongest option is a fully dedicated instance per customer. Shared infrastructure is what keeps cloud contact center pricing low. Dedicated or on-premise deployment is the usual alternative for regulated buyers who need stronger separation between their data and everyone else’s. A procurement team evaluating an AI vendor should ask directly whether conversation data shares storage with other customers, and how encryption keys are separated between tenants. Both answers affect data sovereignty and audit requirements, so they are worth writing into a contract, not just a sales conversation.

An agent scorecard rates a single customer interaction against fixed criteria. Categories typically include greeting and identity verification, accuracy of the resolution, compliance adherence, tone, and completion of required process steps. Each category carries a weight. Category scores combine into one percentage that feeds coaching sessions and QA reporting, and often rolls up into a team-level customer satisfaction index. Reviewed manually, scorecards cover only a small sample of conversations. OnClarity’s AI Agent QA applies a team’s existing scorecard rubric to every conversation across voice, chat, email, and WhatsApp.

QA coverage is the percentage of customer service conversations reviewed and scored against a quality rubric. It measures how much of an operation’s actual customer interaction volume actually gets evaluated. Manual QA programs typically sample only a small slice of contacts. Industry data puts the standard at 2 to 5 calls per agent per month, often a low single-digit percentage of total volume. Everything outside that sample is a blind spot, where compliance breaches, coaching opportunities, and recurring issues go undetected. OnClarity’s AI Quality Agent scores 100% of conversations within about five minutes of call end, roughly 20 times typical manual coverage. That shift turns QA from a sampling problem into a question of rubric consistency and audit-trail completeness.