NUMU CX QA — Omnichannel AI Support Analytics

The problem

A supervisor hears 2% of calls.
Customers hear all 100%.

~2%

Sample-based listening

A traditional QA team gets through a couple of calls per agent per week. The remaining thousands of interactions go unmonitored — including the worst ones.

±2 pts

Subjective scoring

Two supervisors score the same call up to two points apart. Agents distrust the reviews, and appeals eat up hours.

Contact reasons unknown

Why do customers call? Topics are tagged manually and late. A spike only gets noticed once the line is already overloaded.

Capabilities

One product covers
the full contact-center quality cycle.

Every channel in one window

Calls, email, web chat, Telegram, WhatsApp and internal chats — one unified stream of conversations. Every interaction is transcribed and scored, with PII masked before it reaches the database.

Scored today61,402 / 61,402

AI scores every conversation

Automatically, against your quality checklist: greeting, issue resolution, upsell. Mandatory phrases and forbidden promises sit under compliance monitoring — a violation triggers an alert within minutes.

Sentiment and emotions — live

The emotional tone of both sides, interruptions and pauses in real time. Rising negativity is visible before it turns into an escalation or a complaint.

SLA and first response time

Live timings for every channel: never miss the 2:00 threshold on chats. Breaches, queues and peak hours — on one screen with alerts.

Chats · first response01:42 / 02:00

Breached today3 conversations

AI coaching for agents

Growth areas for every conversation, with a predicted effect: how much the score will rise once a specific skill is closed. Benchmarks come from your own line.

Private and on-prem

A local LLM runs inside your perimeter — data never leaves your network. Telephony and CRM integrations: Genesys, Five9, Twilio, Salesforce, Zendesk; export to BI and incentive systems.

Product tour · real screens

A supervisor's morning —
in 60 seconds

These are not renders — live screens from the NUMU QA workspace. What a supervisor opens in the morning instead of random call sampling.

qa.cx.numuhq.com/dashboard

NUMU QA dashboard: average QA score, scoring coverage, critical violations and call sentiment

Average QA score 83% — the line's headline KPI, trended by day

69 critical violations — AI found them on its own across 2,433 conversations

Calls and chats — quality and timings for every channel

The screens are clickable — step through the tour. The full workspace on your own line's recordings — in a live demo: request one →

Closed loop

Caught the negativity?
That is just the beginning.

Monitoring without action is a report for the drawer. The platform closes the loop: from alert to saved customer — with the outcome on record.

14:02AI catches the negativityA sharp sentiment drop or a critical violation — an alert with a quote and a timecode.

14:04The task creates itselfA callback ticket in Salesforce, HubSpot, Zendesk or via webhook — with the conversation context.

14:20Callback by playbookService-recovery scripts in the platform: apology → resolution → compensation by the rules.

14:32Outcome recorded"Customer retained" on the contact card. A dashboard of saved customers and negativity drivers.

A callback within the hour retains most unhappy customers — the cheapest LTV money can buy.

Voice of Customer

Full VoC —
without a third-party survey platform.

Surveys and conversation analytics do not compete — they stitch into one loop. The platform covers both.

01 · NO QUESTIONNAIRES

CSAT on every conversation

AI infers satisfaction from the conversation itself: sentiment, outcome, loyalty signals. Coverage is 100% of interactions — not the 5–10% who answer a questionnaire.

02 · AUTO-SURVEYS

Where a direct answer is needed

A survey builder inside the platform: after a call — keypad IVR rating or a voice bot with speech synthesis; after a chat — a question in the same channel (Telegram, WhatsApp, SMS). Answers are recognized and stitched to the interaction transcript.

03 · PHYSICAL RETAIL

Surveys right in the field

The Field Inside survey module: tablets and QR codes at locations, customer and staff surveys. Results land in the same dashboard as the conversations.

Survey CSAT/NPS and conversation analytics in a single contact profile: one dashboard, one truth, no manual spreadsheet stitching.

Results

Numbers you can see
in your reporting — not just hear about.

CSAT after rolling out QA coaching

average handle time (AHT)

repeat contacts on the same issue

faster resolution of disputes and appeals

lower quality-monitoring costs

compared with mystery shoppers, sample-based listening and other manual tools — while covering 100% of conversations instead of 1–2%.

Mechanics on an example: 300 agents × 60 interactions/day × 10 min of manual review ≈ 375 QA supervisors (≈ $6M/year) — the platform does it automatically · run it on your own numbers →

Figures are the lower bound across real NUMU QA client projects in year one. The effect depends on line size and baseline processes — we will show the range for your contact center in a demo.

Methodology

The questions every
QA leader asks.

How closely will AI scores match my QA team's scores?+

We start with calibration on your recordings: AI and your auditors score the same pool of calls, and we work through discrepancies criterion by criterion — until agreement reaches 95%+. For the first month you can run dual scoring (AI + manual sampling) and track convergence in a dedicated report. After that — a monthly sample cross-check.

How accurate is transcription on telephony and in chats?+

On telephony — 97%+ word accuracy with speaker separation (including narrowband lines and headsets); difficult recordings get a quality flag and do not distort scoring. Chats and email are text with no transcription needed — there, scoring accuracy is limited only by the checklist.

What if an agent disagrees with a score?+

Appeals are one click away from the call card: the agent flags the disputed item, the supervisor re-listens to the moment by timecode and enters a "QA auditor override". The final word always belongs to your team; every override is logged and fed back into training — the model adapts to your reading of the standards.

Can we use our existing scorecards?+

Yes — we migrate your checklists 1:1: category weights, critical items, queue applicability. Historical manual scores are imported, so your trends are not reset. No scorecard yet? We provide an industry template and configure it together during calibration.

What about false positives on critical violations?+

Every alert arrives with a quote and a timecode — verification takes seconds. Sensitivity thresholds are configurable per violation type; a false positive is flagged in one click and fed back into training. After calibration, critical-alert precision stays above 90% — and keeps improving on your data.

Every call. Every chat.
Every agent — under AI analysis.

A supervisor hears 2% of calls.
Customers hear all 100%.