What QEval® looks like from your seat.
A single customer conversation answers a different question for everyone who touches it. Pick the seat closest to yours and see what QEval® actually does for it, in the language and the numbers that seat lives by.
Find your seat. See your view.
Choose the role closest to yours. Every view is the same platform reading the same conversations, shown in the terms that seat actually uses.
Your board is not asking about QA scores.
You signed off on the AI program. Now the question in the QBR is simpler and harder: what did it return? Quality dashboards tell you agents followed the script. They do not tell you about the churn you prevented, the revenue you held onto, or the headcount you freed, so you end up assembling that story by hand from four teams that each measure something different.
See the 82% a QA tool never counts
Quality is one of six layers in every call. The other five (customer, revenue, operational, training, strategic) were $5.3M of the $6.5M in the anchor deployment. That is the part you take to the board.
The Six Layers →One scorecard, even as your AI vendors change
Sierra today, something else in eighteen months. Because human and AI agents are scored the same way, switching agent vendors does not reset your quality program or your data.
AI Agent QA →A number you can defend, not adjust
94%+ accuracy is in the contract, payback is a documented 120-day average, and you can exit in 60 days. The downside is bounded before you ever present it.
Why QEval® →Same budget, higher volume. Make the math work.
Volume is up, the headcount req is frozen, and handle time is drifting the wrong way. A real slice of every call is dead air while the agent hunts through systems and types notes after hang-up. And supervisors, stretched across fifteen or twenty agents, can only spot-check a few calls a week, so most coaching is a guess.
The busywork leaves the call
Real-Time Agent Assist surfaces the answer, drafts the wrap-up note, and fills the disposition while the agent stays with the customer. Handle time typically falls 25 to 30% and first contact resolution rises 8 to 12%.
Real-Time Agent Assist →Every supervisor coaches on evidence
Because 100% of calls are scored, coaching points to a real moment, not a hunch. One supervisor reaches roughly three times the agents, and retention climbs 40%.
Coaching and performance →Capacity you find instead of hire
Shorter handle time and fewer transfers freed about 45 FTE-equivalent in the anchor deployment, capacity you redeploy instead of backfill.
Operational intelligence →Two percent of calls, and the agents know which two.
You staff a team to hand-score a sliver of interactions, and even then two scorers grade the same call differently. The sample is too small to be fair and too slow to catch a problem while it still matters. Most of the week disappears into grading, not into making anyone better.
100% scored, at an accuracy you can sign
Auto QA grades every interaction at a 94%+ accuracy SLA in the contract. General-purpose tools tend to sit at 65 to 70%, which is why nobody trusts them unsupervised.
Auto QA →Calibrated to your scorers, with the receipt
Every score links to the exact line in the transcript that earned it, and the model is tuned against your humans. When you disagree, you can see precisely why.
How scoring works →Your analysts get their week back
When grading is automatic, analyst capacity rises about 65%. The team moves to calibration, dispute review, and the calls that genuinely need judgment.
Coaching and performance →The violation you never heard is the one that fines you.
A missed disclosure on call 4,312 is a finding whether or not anyone reviewed it, and sampling leaves most of your exposure unseen. On top of that, every time a new AI tool shows up, your security team asks the question you cannot hand-wave: where, exactly, does the customer data go?
Every call checked, not the lucky sample
Required disclosures, script adherence, and risk language are flagged across 100% of interactions at 95%+ recall and 98%+ accuracy. One deployment cut violations 85%.
Compliance and redaction →PII is gone before the model runs
Names, card numbers, and health details are stripped at ingest by Named Entity Recognition. The model is built and run in-house, so customer data never trains a third-party AI.
Security and trust →Audit-ready, line by line
SOC 2 Type II, ISO 27001, ISO 42001, PCI DSS Level 1, HIPAA, GDPR, and CCPA, and every score traces back to the transcript moment that triggered it.
Certifications →The why behind your CSAT is on the calls, not the survey.
Your survey hears from a few percent of customers, mostly the delighted and the furious, and it arrives weeks after the moment. Meanwhile the broken policy, the confusing fee, the handoff that dropped the ball, get said out loud on hundreds of calls a day and never reach the team that could fix them.
Voice of the customer from every call
Sentiment, intent, and the reason behind a low moment are read on 100% of conversations as they happen, not sampled weeks later through a survey.
Customer intelligence →See churn before the cancellation
Each interaction carries a predicted CSAT and a churn-risk read, so at-risk accounts surface while you can still do something about them.
Layer 2: Customer intelligence →Root causes routed to the owner
Recurring issues are clustered and sent to product, marketing, or ops, the people who can fix the cause, instead of dying in a CSAT report.
The Six Layers →Coaching once a month does not change a habit.
Your best practices live in three veterans' heads, new hires take months to get there, and the monthly coaching session is about a call from three weeks ago that nobody quite remembers. Feedback arrives long after the moment, so behavior rarely moves.
Coaching fires on the moment, not the month
The HI Model finds the exact skill gap in a real call and turns it into a focused action while it still matters. Coaching frequency rises about 300%.
Coaching lifecycle →The right lesson, attached to the gap
When a gap appears, QEval LMS assigns the matching lesson automatically, so training is about what the agent struggled with this week, not a generic module.
Training and LMS →A skill map for the whole floor
Gaps are ranked by business impact, so you coach what moves outcomes first and watch the before and after instead of hoping.
Layer 5: Training intelligence →Judged on your real work. Helped while it counts.
You take hundreds of calls and get graded on three, sometimes your worst three. The feedback lands a week later and tells you what, not how. And in the middle of a hard call, the answer you need is three tabs and a wiki search away while the customer waits.
Every call counts, scored the same way
All of your conversations are graded on the same criteria, so your score reflects your real work, not whichever handful a reviewer happened to pull.
How scoring works →Backup while you are still talking
Real-Time Agent Assist surfaces the answer and writes the notes in the moment, so you stay with the customer instead of digging.
Real-Time Agent Assist →Feedback you can use, credit you can see
Coaching points to the exact moment and the next move, and good work shows up on leaderboards and recognition instead of going unnoticed.
Coaching and recognition →Seven views. One set of conversations.
The views differ because the jobs differ. The data does not. Every conversation is read once, by one proprietary model, and sorted into six layers of intelligence. Each seat is really just leaning on a different layer. Most QA programs stop at Layer 1, which is about 18% of the value.
The same rollout. A different win at each seat.
A Fortune 500 automotive enterprise, five brands, 1,200 agents, in six months. One platform, one contract. Here is roughly what landed for each seat.
The things people actually ask.
Why does the same platform need seven different views?
Because the same conversation answers seven different questions. The executive needs the dollar figure, the QA lead needs calibration, the agent needs help mid-call. It is one dataset, read seven ways, not seven products bolted together.
Is this seven tools, or one?
One. Auto QA, Real-Time Agent Assist, coaching, compliance, and customer intelligence are all capabilities of the same engine reading the same conversations. One deployment, one contract, one scorecard.
We already get QA from our CCaaS. What is actually different here?
CCaaS-native QA usually scores Layer 1 on a small sample at 65 to 70% accuracy. QEval scores 100% of interactions at a 94%+ contractual accuracy SLA and reads the other five layers, which is where about 82% of the value showed up in the anchor deployment.
How long before each team can see its own view?
30 days to deploy, contractual. A documented average of 120 days to ROI, with 60-day exit rights if it does not land. Every seat goes live on the same timeline, not one team at a time.
Want to see your view?
Bring whoever sits in the room. We will score a couple of your real calls and show each person the part that lands for them. No slides.