Customer Support
HR & Ops
IT Support
Sales & Marketing
What is Auto QA?
Auto QA is the use of AI to evaluate customer-agent interactions automatically. That means scoring calls, chats and emails against defined quality criteria without a supervisor having to select and listen to recordings one at a time. The AI transcribes the interaction, analyzes its content against a custom QA scorecard and produces a score, a summary and specific flags for anything that fell outside the criteria.
Supervisors can only manually review so many calls. Most contact centers sample 2-5% of interactions, leaving 95-98% of potential insights uncovered. Customer sentiment trends, knowledge gaps, opportunities for upsells, most of this goes unreviewed. Compliance violations on unreviewed calls go undetected. Coaching conversations happen on the interactions supervisors happened to pull, not necessarily on the ones that most needed attention.
Auto QA closes that gap by evaluating every interaction, surfacing relevant insights from 100% of calls and interactions. This allows supervisors and agents to review at scale, to easily see what went well and what could be improved and to track trends more accurately over time.
What does Auto QA evaluate?
Auto QA systems apply your QA scorecard criteria to every interaction. Common evaluation categories include:
- Compliance and script adherence: Were required disclosures made? Was the privacy notice read? Were forbidden phrases avoided? Were upsell scripts followed correctly?
- Knowledge accuracy: Did the agent give correct information? Were there moments where the agent appeared uncertain or gave contradictory answers? Did the agent have to put the customer on hold while they searched fro the correct answer?
- Customer experience signals: Was the customer’s issue acknowledged? Were empathy statements used appropriately? Did the agent interrupt the customer?
- Silence and pacing: Were there extended periods of silence that indicate the agent was searching for information? Did hold time exceed thresholds?
- Resolution quality: Was the issue resolved? Was a follow-up committed to and documented? Did the agent verify resolution before closing?
- First contact resolution risk: Based on the interaction content, does this call show signs of a repeat contact, like an incomplete resolution, an uncertain answer or a frustrated sentiment rating?
Auto QA vs. manual QA: what AI changes and what it doesn’t
Auto QA doesn’t eliminate the need for human QA judgment, but it does change where that judgment is applied.
Auto QA is ideal for applying consistent criteria at scale, which is especially helpful for large contact centers and support teams. It can detect compliance requirements automatically, identify patterns across thousands of interaction and flag important findings for human review.
Human QA reviewers are still necessary, especially when it comes to complex interactions that require human interpretation and empathy, handling appeals from agents who dispute a score and making final calls on compliance edge cases.
Auto QA covers more interactions with less manual effort, freeing up supervisors to shift their focus toward agent coaching and optimizing operations with auto QA insights.
How Auto QA connects to conversation intelligence
Auto QA and conversation intelligence are related but distinct. Auto QA is evaluative: it applies a defined scorecard to interactions and produces a pass/fail or scored result. Conversation intelligence is analytical: it surfaces patterns, trends and insights from interaction content that weren’t necessarily defined in advance.
The most effective contact center operations use both: Auto QA for systematic quality management against known criteria, conversation intelligence for discovering what those criteria should be and what’s changing in the operation that the current scorecard doesn’t capture yet.
How Capacity’s Auto QA works
Capacity’s Auto QA evaluates 100% of interactions across voice, chat and email, scoring them against your QA criteria, flagging compliance gaps and surfacing the coaching moments that most need supervisor attention. The insights from Auto QA feed back into the AI Knowledge Orchestration Layer, so that next time, the AI agent might answer the question and deflect the call from escalation, or the agent might have better real-time guidance.
See Capacity’s Auto QA capabilities →
See also:
- Agent assist
- Conversation intelligence
- Sentiment analysis
- See all definitions
Frequently asked questions about Auto QA
On objective criteria (compliance disclosures, required phrases, script adherence), AI scoring is highly consistent and typically more reliable than human scoring because it doesn’t vary by reviewer or shift. On subjective criteria (empathy, tone appropriateness, the quality of an explanation), AI scoring is improving but still benefits from human calibration. Most mature deployments use a hybrid: AI handles objective criteria automatically, human reviewers focus on subjective criteria for flagged interactions.
Well-designed auto QA systems include a review and appeal workflow. The agent or supervisor can flag an interaction for human review, add context and request a score adjustment. The AI score is treated as a first pass, not a final verdict, particularly for edge cases, novel situations or interactions where the criteria were ambiguous. A dispute process is important both for accuracy and for agent trust in the system.
