The Performance Management Platform for the AI-era contact center.
QEval® scores 3+ billion conversations every year against your scorecard. Real-time agent assist, audit-ready compliance, AI-led coaching, predictive analytics. All from a proprietary closed-source model built for enterprise. Not an LLM wrapper.
Tests whether QEval® rewards a fast repeat-contact acknowledgement, refund resolution, and supervisor pathway while still watching disclosure and churn risk.
One interaction. Multiple expert judgments. One defensible scorecard.
QEval® combines contextual LLM reasoning, machine-learning signal models, and rules-based QA logic inside a proprietary evaluation intelligence layer. Each node below is a specialized expert routed through the engine before the final scorecard is produced.
Manual QA, keyword analytics, rules engines. All three share the same fatal flaw.
Every generation of QA solved its predecessor's problem and inherited a new one. The fourth generation is the first that scores meaning, not just words.
- Evaluates 1 to 3 percent of interactions
- Subjective, inconsistent scoring across reviewers
- Feedback delivered weeks late, after the customer is gone
- Flags calls by words or phrases, no context
- Cannot distinguish sarcasm, sentiment, or intent
- High false-positive rate buries the real signal
- Predefined decision trees authored by humans
- Breaks the moment patterns shift
- 65 to 70 percent real-world accuracy on novel data
- Contextual understanding across 100 percent of conversations
- LLM reasoning, ML signal models, and QA logic combined
- Proprietary multi-expert evaluation architecture
- 94 percent classification accuracy, contractually committed
A 500-seat contact center loses $2.4M a year to QA blind spots.
Whether those blind spots come from manual sampling, keyword mismatches, or inaccurate classification, the cost compounds every quarter. Most teams have never seen the math.
Estimate based on 500 inbound seats, 2 percent QA sample, 0.4 percent actionable-issue rate, $750 average cost per missed issue, and documented productivity reclaim from 100+ QEval® deployments. Adjust your own assumptions in the Business Case Builder below.
Eleven capabilities. One scorecard. Every conversation.
Human agents, AI agents, voice bots, chat copilots, IVR flows. QEval® scores them all, surfaces what matters, and ties each interaction to a business outcome. Replaces four-to-six-point solutions in a single platform.
→ Agent notes: "save attempt"
→ Outcome: retained
Guidance that feels like a second screen, not another dashboard.
QEval® listens for the call moment, scores the risk, and gives the agent the next best line before the QA miss happens. The same model that grades the call afterward powers the guidance during the call.
Last three contacts surfaced from CRM. Sentiment baseline captured. Refund policy pinned. The agent starts with context instead of opening tabs.
"I see this is your third contact, so I will not make you repeat it. I am opening the order and refund policy now."
QA scores are one layer. Five more drive 87% of the value.
Most QA programs measure Quality and Compliance and stop. The Six Layers of Intelligence framework extends evaluation to customer, revenue, operational, training, and strategic intelligence. Measured across 100+ enterprise deployments since 2022.
Turn QA coverage into a finance-ready investment case.
Six industry presets, three scenarios, eight buyer inputs. Output is your exposure, your payback, your year-one value. No QEval® internal margin math, no published value-waterfall. Your numbers, your model.
Model the board memo before the sales call.
With 240,000 monthly conversations at 2% QA coverage, your program leaves 235,200 conversations outside review each month. The Base scenario captures 18% of addressable value across four levers, paying back in under a month with $4.5M in year-one benefit.
Three roles. Three reasons to deploy.
Each persona evaluates QEval® on different criteria. Each finds proof on this page. Each gets a dedicated workspace once deployed.
CX, QA, Operations.Scorecards that survive the floor.
QEval®'s evaluation engine was trained on the rubrics our QA team used in live operations, not a textbook QA framework. 94%+ classification accuracy is the bar an enterprise supervisor would accept.
CFO, Procurement.Break-even at Month 3.
Value across all six intelligence layers. Consumption-based billing aligns with operational seasonality. Accuracy commitments are written into the master agreement, not the presentation.
CIO, CISO, Architecture.Sovereign by design.
QEval® runs on a proprietary closed-source multi-expert evaluation architecture. Customer data never enters foundation model training. PHI and PII are redacted via Named Entity Recognition before any LLM processing.
The dimensions where traditional QA platforms consistently fall short.
Comparison based on publicly available vendor documentation as of 2026. We do not name competitors in customer materials. The rows below are factual and defensible. Bring your own RFP rubric.
| Capability | QEval® | Typical QA / QM platforms |
|---|---|---|
| Foundation AI model | Proprietary multi-expert (MoE) | Generic LLM wrapper or undisclosed |
| Classification accuracy commitment | ✓ 94%+ written into the contract | Undisclosed or 70 to 75% in marketing |
| Pre-transcription PHI / PII redaction | ✓ Native, at ingest | Post-transcription only, if at all |
| Coverage flexibility | 30 to 100% throttle by program | All-or-nothing licensing |
| Deployment timeline | 30 days, money-back guarantee | 90 to 180 days, no guarantee |
| Exit clause | ✓ 60-day exit, no penalty | 12 to 36 month lock-in |
| Real-Time Agent Assist | Integrated, same model | Add-on, separate vendor, or none |
| Channel coverage | Voice, chat, email, SMS, case notes | Voice + chat at best |
| Case notes & CRM text analysis | ✓ Native | Custom build required |
| Built-in survey platform | ✓ Native CSAT and NPS | Third-party integration |
| Native BI / Analytics | Six-Layer Intelligence BI | Basic dashboards or reporting |
| Behavioral coaching protocol | HI Model 90-day lifecycle | AI-assisted or basic flags |
| AI agent scoring (Sierra, Decagon, Agentforce) | ✓ Vendor-neutral, same scorecard | Not supported |
| EU AI Act compliance | ✓ Compliant | Partial or in progress |
ICMI Best New Technology Solution. 2025.
External validation paired with category recognition. ICMI for technical originality. CMP Research for category leadership. The proof story is operational, contract-backed, and independent.
Enterprise-grade. Zero exceptions.
QEval® handles regulated and identifiable customer data every minute of the day. The certifications are the floor. The architecture is the proof. Pre-transcription redaction means PII and PHI never reach an LLM.
Four numbers no peer publishes.
The questions buyers actually ask.
What makes QEval® a Performance Management Platform, not just QA software?
QEval® ships eleven capabilities in one platform: AI Evaluation, Real-Time Agent Assist, the HI Model coaching lifecycle, Compliance and pre-transcription redaction, Speech Analytics, Vision Model with screen capture, Surveys and CSAT intelligence, Analytics and BI, Gamification, Case Notes Analysis, and the Universal Connector. Most enterprises replace four to six point solutions when they move to QEval®. Quality is one layer of six. The other five layers (customer, revenue, operational, training, and strategic intelligence) deliver 87% of measured value.
We already QA our human agents. Why add a layer for AI agents too?
AI agents do not grade themselves the way you grade humans. Containment, deflection, and resolution metrics from AI platforms do not measure against your scorecard, your brand voice, your compliance rules, or your retention metrics. QEval® is the vendor-neutral layer that scores AI and human agents on the same scorecard, so quality, compliance, and coaching stay consistent across your entire contact center, whoever or whatever delivered the interaction.
What about PHI, PII, and regulated data?
PHI and PII redaction runs at ingest via Named Entity Recognition, before any data reaches an LLM. Original recordings are deleted after redaction. Only redacted versions retain a full audit trail. QEval® is SOC 2 Type II, PCI DSS Level 1, HIPAA-ready, GDPR-compliant, ISO 27001 and ISO 42001 certified. Regulated customers make up the majority of our enterprise base.
How does Real-Time Agent Assist work?
RTAA listens to the live conversation, detects intent and sentiment, and surfaces guidance to the agent while the call is happening. Compliance prompts fire before a disclosure window closes. De-escalation scripts surface when frustration is detected. Next-best-action talk tracks appear when an upsell opportunity emerges. The same model that grades the call after it ends powers the guidance during the call. Standard documented outcomes: 25 to 30% AHT reduction, 8 to 12% FCR improvement, 4 to 5% CSAT uplift, 45-day POC.
How fast can we be in production?
Standard deployment is 30 days, contractually committed with a money-back guarantee. Enterprise rollouts across multiple lines of business, custom scorecards, and AI agent integrations typically run 60 to 90 days. The buying case is modeled around a 120-day ROI window so finance, operations, and QA can validate value before expansion.
How is this different from the analytics our CCaaS already ships?
Native CCaaS analytics measure containment, deflection, and reporting metrics defined by the platform vendor. QEval® scores against your scorecard, your brand voice, your compliance rules, and your business outcomes. It is also vendor-neutral, so the same scorecard works across Genesys, NICE, Five9, Sierra, Decagon, Agentforce, and your in-house GenAI without rewriting rubrics per platform.
Human, AI, or anything in between.
Bring your scorecards, your AI agents, your CCaaS. We will score a real call in 30 minutes and show you what your current program missed last week.