Quality assurance on 100% of interactions, not a 2% sample.

Most contact centre QA runs on manual sampling — a QA analyst listens to a handful of calls per agent per month, scores them against a rubric, and extrapolates from there. It's the only approach that scales with a human-only QA team, but it means the vast majority of interactions are never reviewed at all, and systemic issues can run for months before a small sample happens to catch them.

Almanor AI's QA model scores 100% of interactions — every call, chat, and email, across every agent and channel — using AI-driven analysis calibrated against your specific quality rubric. This surfaces patterns a 2% sample simply can't: a specific issue type where resolution quality is consistently weak, an agent trending toward burnout before it shows up in attrition, a new complaint pattern in its first week rather than its third month.

Human QA specialists remain central to the process — calibrating the AI scoring against real judgment, reviewing the interactions AI flags as borderline or high-risk, and running coaching conversations with agents. The AI layer doesn't replace human QA judgment; it means that judgment gets applied everywhere instead of a thin, random slice.

Every call, chat, and email scored against your quality rubric — not a sample, the entire population of interactions.

Structured coaching built from real, comprehensive performance data rather than a handful of sampled calls per month.

Monitoring for regulatory and compliance adherence across 100% of relevant interactions, with full audit trail.

Pattern identification across your full interaction volume — surfacing systemic issues before they show up in satisfaction scores.

AI scoring calibrated against your specific quality standards, refined collaboratively with your QA and operations leadership.

Real-time visibility into quality trends by agent, team, channel, and issue type — not a monthly retrospective report.