Contact Center AI Platform & Analytics | Simetrix
Zentara · The intelligence platform behind every Simetrix operation

The operating system for customer experience.

Zentara is an AI-native intelligence platform that analyzes every scoped customer interaction. Not a QA tool. Not an analytics overlay. The operating model. Built by operators on their own customer operation first, scored against their own work, then deployed across every Simetrix client engagement.

Included in every Simetrix engagement. No separate licensing, no implementation discount we negotiate.

Zentara platform · live
Built on our operation first
zentara.app / dashboard / liveLive
100%
Interactions analyzed
vs 3-5% sampling
8
AI engines live
Voice to compliance
XLA
Experience Level Agreement
Composite scoring
AI engine accuracy · production
Speech-to-Text
98%
Sentiment Analysis
94%
AI Quality Scoring
97%
Churn Prediction
87%
Burnout Prediction
81%
Platform activity · last 60 minutes
14:32XLACall scored 87/100, quality preserved
14:31SENTFrustration signal at minute 6, coaching alert sent
14:30CHURNHigh churn intent detected, save desk routed
14:29COMPDisclosure language flagged for review
100%
Of scoped interactions analyzed
8
AI engines in production
XLA
Experience Level Agreement scoring
3-layer
Cost-efficient architecture
Real time
Agent coaching and alerts
10,000+
Calls analyzed across production
The shift the industry has not made

From SLA to XLA.

Service Level Agreements measure what an agent handled. Experience Level Agreements measure what the customer actually experienced. Most BPO operations still report on the former. Zentara was built to run on the latter.

Legacy SLA model

What an agent did

  • AHT, ACW, hold time, calls handled per hour
  • 3-5% of calls sampled by QA team
  • Weekly reports, often a week stale
  • Quality is a number from a rubric, not a customer outcome
  • Compliance reviewed on a sample, missed at scale
  • Churn intent invisible until customer is already gone
Zentara XLA model

What the customer experienced

  • CSAT, FCR, sentiment, NPS, resolution quality, CES composite
  • 100% of scoped interactions analyzed, every one
  • Real-time dashboards, alerts in seconds
  • Quality is a customer outcome, calibrated to the workflow
  • Compliance signals surfaced on every call
  • Churn intent flagged in the moment, save desk routed instantly
Platform architecture

Three layers that never swap jobs.

Cost-efficient by design. Layer 1 writes intelligence objects once per call. Layer 2 aggregates at zero AI cost. Layer 3 reads pre-built summaries through natural language. The architecture is what makes 100% interaction analysis financially viable, not 3-5% sampling.

1

Ingestion layer

Every scoped call, chat, and email enters the platform here. The 8 AI engines write intelligence objects once per interaction: transcript, sentiment, quality score, churn signal, compliance flags, burnout indicators. Computed once, stored forever.

Cost model: Per-interaction AI cost, paid once.
2

Aggregation layer

PostgreSQL with TimescaleDB rolls up the intelligence objects into pre-built summaries: by agent, by team, by industry, by time window, by workflow. Aggregates run on a schedule, no AI inference required. This is where 100% analysis becomes affordable.

Cost model: Database queries, zero AI cost.
3

Query layer (Cora)

Cora is the natural-language BI interface. Operators ask business questions in plain language and Cora reads the pre-built summaries. Never re-processes raw data. Never touches the AI engines for repeat questions.

Cost model: Reads aggregates, AI only for query parsing.
Eight AI engines in production

Voice to compliance to burnout, scored on every interaction.

Each engine is purpose-built for the signal it surfaces. Accuracy numbers below reflect production performance against ground-truth labels on the Simetrix operation.

Speech-to-Text · 98%

Deepgram-powered transcription, calibrated for customer support call audio across 20+ languages. Speaker diarization, timestamps, confidence scores. Powers every downstream engine.

Sentiment Analysis · 94%

Per-utterance sentiment scoring across the call timeline. Detects escalation moments, frustration patterns, satisfaction inflection points. Surfaces the moment-of-truth, not just the call average.

AI Quality Scoring · 97%

Workflow-calibrated quality scoring against the client QA rubric. Replaces 3-5% human sampling with 100% AI scoring, audited by humans on a calibration sample to maintain rubric drift control.

Churn Prediction · 87%

Predicts churn intent from conversational signals: language patterns, sentiment trajectory, escalation history, repeat contact patterns. Surfaces high-risk interactions for save desk routing in real time.

Burnout Prediction · 81%

Industry-first agent burnout prediction. Detects the conversational patterns that precede attrition: tonal flattening, empathy decline, energy drop. Lets operators intervene with coaching before agents leave.

Compliance Detection

Pattern-based compliance signal detection: TCPA disclosure language, HIPAA-aligned identity verification, UCSPA-aware claim timing, KYC verification quality. Calibrated per industry per workflow.

XLA Calculation

Composite Experience Level Agreement score. CSAT 25%, FCR 20%, Sentiment 20%, NPS 15%, Resolution Quality 10%, CES 10%. Scored on every interaction, aggregated by agent, team, industry, time window.

Real-Time Agent Assist

Live coaching during the call. Detects frustration moments, compliance-risk phrasing, churn intent signals, and surfaces contextual guidance to the agent in milliseconds. Powered by live inference.

The XLA composite score

What we actually measure.

The XLA score is a weighted composite of six customer-experience metrics. Each component is calibrated per industry, per workflow, with hard caps where structural signals override surface scores.

XLA composite weights
CSAT (Customer Satisfaction)
25%
FCR (First-Call Resolution)
20%
Sentiment trajectory
20%
NPS (Net Promoter Score)
15%
Resolution Quality
10%
CES (Customer Effort Score)
10%
Hard caps and overrides
  • If repeat contact same issue within 72h, FCR auto-capped at 30 regardless of surface score
  • If compliance violation detected, full XLA capped at 50
  • If customer escalation requested not delivered, FCR auto-capped at 40
  • Industry-specific weights adjustable per client engagement
XLA scoring requires structured operational signals from the CRM and telephony layer in addition to transcript content. Score quality scales with system integration depth.
Cora · Natural-language BI

Ask the platform in plain English.

Cora is the conversational query layer on top of Zentara. Operators ask business questions in natural language and Cora returns answers from pre-built summaries. Not a chatbot. A BI interface for the people who run customer operations.

Question
"Which agents had the most frustration signals this week, and what workflows were they on?"
Cora response
Three agents flagged this week with rising frustration signal density. Agent #042 on the retention queue (28 sessions, frustration peak at minute 4). Agent #117 on the technical support queue (19 sessions, escalation pattern Tier 1 to Tier 2). Agent #064 on the billing dispute queue (22 sessions, sentiment recovery weak after minute 6). Want me to route coaching alerts or show me the call samples?
Question
"What is our XLA score trend across all retention calls this quarter?"
Cora response
Retention queue XLA trending up across the quarter. Started at 68.2 in week 1, current week 78.4, peak 81.0 in week 9. The lift is driven primarily by improved sentiment recovery (from 62 to 76) and FCR ticking from 71% to 79%. Want me to break this down by save-reason category or by save-desk agent?
Cora reads aggregates, never re-processes raw call data. This is what makes natural-language BI affordable at scale.
Production proof

What the platform surfaces, at scale.

100%
Scoped interactions analyzed
8
AI engines in production
Real time
Coaching and compliance alerts
XLA
Composite scoring on every call
PLATFORM CAPABILITY

The signals legacy QA cannot surface at 3-5% sampling.

When a call gets flagged for review under traditional QA, the team sees a quality score. The platform sees the full picture: per-utterance sentiment, escalation moments, churn intent inflection, compliance language drift, agent burnout patterns, revenue opportunities not pursued. Every signal computed on every interaction, surfaced in real time to the operator running the program.

ComplianceSurfaced before liability
Churn intentFlagged in the moment
BurnoutDetected before attrition
RevenueOpportunities captured live
See the platform live

Book a Zentara walkthrough.

30 minutes on a live dashboard. Real production data from our operation. No slides, no demo environment, no decks. Just the platform doing what it does.

Book a walkthrough

See Zentara on a live operation.

30 minutes with our operations team. Real production dashboards, not stock demo data. We show you the engines on a live operation and answer technical questions directly.

The platform is included in every Simetrix engagement. There is no separate Zentara contract or licensing path for clients who run their operations with us.

We respond within one business day. Our operations team runs every review personally.

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