QA that covers 100% of interactions — not the 2% manual review can reach

Voice interactions are the hardest channel to QA at scale. Listening to calls takes time, extracting signals from audio requires different tooling, and most QA programs simply skip voice or cover it with a tiny sample. Voice Agent changes this by transcribing calls, extracting tonal and emotional signals, and running the same 54+ ML model suite across voice that Elevate SX applies to written interactions — bringing voice fully into scope for QA and coaching programs.

Voice Agent capabilities

Elevate SX includes both Auto QA and Manual QA as complementary layers. Auto QA scores every interaction automatically using the 54+ ML model suite — generating an Auto QA Score for each ticket and call without human involvement. Manual QA provides the structured infrastructure for human reviewers when deeper review is warranted, including Custom Scorecards tailored to your quality standards, Arbitration workflows for disputed scores, and Grade the Grader to ensure reviewer calibration over time.

Auto QA

• Scores every interaction automatically — 100% coverage, no sampling
• Auto QA Score generated from the full ML model suite including sentiment, resolution detection, grammar, tone, and compliance signals
• CES (Customer Effort Score) prediction based on interaction signals
• GenAI summary of each interaction for fast QA review without full re-read
• AI suggestions surfaced to agents and managers based on score patterns
• Agentic reasoning — the system explains the basis for each score, not just the number

Manual QA infrastructure

• Custom Scorecards — build QA rubrics aligned to your specific quality standards and product areas
• Arbitration — structured workflow for resolving disputed QA scores between agents and reviewers
• Grade the Grader — calibration tool that evaluates reviewer consistency to ensure QA scores are fair and comparable across the team

Zero Tolerance Policy enforcement

Auto QA automatically flags interactions containing profanity, discriminatory language, or other zero-tolerance policy violations — across 100% of interactions. These violations are surfaced immediately rather than waiting for a manual sample to catch them by chance.

Every Auto QA score in Elevate SX is generated by a suite of more than 54 machine learning models, each trained to detect a specific quality signal from support interactions. Rather than using a single general-purpose model to evaluate everything, SupportLogic’s approach applies purpose-trained models to each signal category — producing more accurate, explainable, and actionable scores than generic LLM-based QA.

The blog post Why Deep Sentiment Analysis Is Foundational for Auto QA describes SupportLogic’s 3-layer NLP approach that underpins these models — transforming quality monitoring from a compliance checklist into a predictor of CSAT and customer retention.

Signal categories covered across the 54+ models

Why purpose-trained models matter for QA

Generic large language models applied to QA produce scores that are hard to explain and harder to act on. When an agent asks “why did I get that score?”, a black-box LLM answer creates disputes and erodes trust in the QA program. SupportLogic’s purpose-trained models produce scores with agentic reasoning — explaining which specific signals drove the outcome — making coaching conversations concrete and defensible. Read more in the blog post Why Deep Sentiment Analysis Is Foundational for Auto QA →

Scoring every interaction only creates value if those scores surface meaningful patterns. Elevate SX includes QA Analytics and Performance Trends dashboards that aggregate Auto QA and Manual QA scores into team-level, agent-level, and interaction-type views — giving QA leads and support managers the visibility to identify systemic issues, track improvement over time, and report on quality program effectiveness to leadership.

What QA Analytics surfaces