The product, in motion.
Watch one conversation pass through ingest, redaction, summarization, sentiment, classification, scoring, coaching, training, Next Best Actions, Six Layers attribution, and real-time alerting. Nine steps. Three minutes. No signup. No demo call required.
The call arrives over an authenticated, encrypted channel.
OAuth 2.0 bearer-token authentication from the CCaaS. AES-256 at rest. TLS 1.2+ in transit. Stored temporarily on an IP-restricted Etech server, region-local. Hosted on AWS today; Azure and GCP next. No model has read a word yet.
Two things happen in 800 to 1200 ms. Sensitive data removed, useful entities tagged.
The NER pipeline runs in both directions at once: PII, PHI, and PCI are masked into typed tokens; non-PII entities (intent, product, account, organization) are extracted as structured tags for the MoE to consume. The original media is deleted at the end of this step.
Speech-to-text with speaker diarization. LLM-generated structured summary on every call.
Mono-channel capable. Speaker turns labeled. The MoE drafts a structured summary every interaction (purpose, key events, resolution, follow-up). About 60 seconds of wrap-up time saved per call. 326M classifications every 5 minutes across production. 35+ languages including LATAM Spanish, Hindi, and Tagalog.
Sentiment is a trajectory, not a single number. And it predicts what happens next.
Multi-dimensional sentiment tracks empathy, frustration, and resolution across turns. Speech analytics layer adds silence detection, talk-over flags, and 500+ intent categories tracked automatically. Predictive outcomes on top: predicted CSAT, churn risk, dispute risk. Predicted scores fill the gap on the 80%+ of calls customers never survey.
The MoE routes each scorecard item to a vertical compliance expert. Then the call is graded.
The Classification Engine routes by intent + vertical. For this Financial Services call, the Financial expert sub-model handles disclosure, suitability, and Reg E checks. 94%+ classification accuracy SLA, contractual. Inter-rater agreement targets 100% against your QA team's grade. Six lanes shown here; twelve verticals on the Compliance page.
Auto Coach AI works both ways. Supervisor side and agent side.
The score is the start of the work, not the end. Auto Coach AI gives the supervisor an evidence-anchored coaching recommendation, and at the same time tells the agent which behavior to focus on, the potential impact, and routes them the right training. Same engine, two audiences. 40% faster agent improvement cycles documented in production.
One score routes the right action to the right role automatically.
Automated Training Allocation assigns the agent a targeted LMS module based on the scored skill gap. Then per-role Next Best Actions fan out so every person in the chain knows what to do, with an estimated impact attached. The system doesn't just flag a problem; it routes the fix.
"Balance verification protocol" module assigned to Aria.
Auto-allocated based on the compliance gap identified at turn 3. Estimated completion: 18 min. Re-test on next 5 calls.
One conversation. Six Layers. Value attributed to each.
The Six Layers framework attributes business value beyond Layer 1 QA. For this one call, here's where the value lives, in dollars. QA captured $48. The other five layers captured $1,817.
Steps 1 to 8 process the call after it ends. This happens while it is still going.
The same MoE that scores the call retrospectively also flags critical violations live, in parallel with ingestion. Same engine, two timelines. 380 ms from violation flagged to alert dispatched. Five channels, customer-configurable per program.
One call. Nine steps. $1,865 attributed across six layers.
QA captured $48 of it. The other five layers captured the rest. Real product interface, real scoring, real summaries, real attribution. The same components your team uses in production.
Three threads. One continuous loop.
Each capability shown in the tour has its own deeper reference page. The whole platform connects through one MoE, one redaction sequence, and one attribution model.
AI Agent QA
The tour graded Aria, a Voice AI agent. The AI Agent QA page covers Sierra, Decagon, Agentforce, Ada, Phonely, Floatbot, and in-house GenAI on the same scorecard.
See AI Agent QACompliance + Redaction
Custom NER entity selection. 800-1200 ms ingest redaction. 380 ms real-time alerts. Twelve vertical compliance experts.
See the architectureSix Layers of Intelligence
82% of value comes from Layers 2-6. The tour proved it on one call. The framework explains it across a whole program.
See the frameworkBring your conversations. We will show you $1,865 calls.
Send the tour to your VP, then book a working session. We will run QEval® against a real call from your operation, ship the attribution back, and pilot toward ROI in 120 days, contractually.