01
Full monitoring of all interactions
Monitor voice, SMS, WhatsApp, email, and Teams interactions to help ensure they meet your guidelines, standards, and customer-care expectations.
Conversation intelligence for enterprise & contact centers
Your first Quality Assurance (QA) Supervisor for every customer interaction.
QA Supervisor analyzes interactions across voice, SMS, WhatsApp, email, and Teams — enabling organizations to move from reviewing limited call samples to supervising up to 100% of customer interactions across human and AI-assisted operations.
Designed for contact centers and enterprise support teams that require scalable quality assurance, compliance visibility, and AI-readiness across every communication channel.
Proprietary technology by Reality Border, a subsidiary of IQSTEL.
Recommended pilot: one service line · one QA guideline · 2–4 weeks of interactions
Benefits
Close quality assurance gaps across every customer interaction.
QA Supervisor gives contact services and customer care teams a practical way to increase monitoring coverage, reduce operational risk, and improve quality without expanding manual review work.
02
Close the Quality Assurance gaps
Move beyond small samples and identify missed disclosures, script deviations, weak answers, and recurring service issues across a broader interaction set.
03
Reduce cost
Increase QA coverage without requiring proportional growth in manual review hours, helping supervisors focus on decisions, coaching, and escalations.
04
Reduce human errors
Apply consistent evaluation criteria across interactions and surface evidence that helps teams correct problems faster and more reliably.
Why this matters now
Most contact centers are making high-risk decisions from a tiny sample of calls.
Manual QA gives useful judgment, but it rarely gives enough coverage. AI voice projects add another risk: teams automate before they understand what their best human agents actually do.
01
QA blind spots hide operational risk
Sampling a small percentage of calls means missed disclosures, broken scripts, recurring objections, and poor customer experiences remain invisible until they become expensive.
02
Supervisors lack evidence at coaching time
Managers need exact moments, not generic averages. QA Supervisor turns conversations into traceable examples for recognition, training, escalation, and process correction.
03
AI pilots fail without a human-performance baseline
Before replacing or augmenting agents, teams need to know which call flows are predictable, which require escalation, and which quality checks must never be skipped.
The QA Supervisor platform
From recordings to operational decisions.
QA Supervisor ingests customer interactions, structures them, evaluates them against configurable criteria, and organizes the results around the decisions contact-center leaders actually need to make.
- ✓Use existing recordings, messages, and interaction exports through file-based or API-based intake.
- ✓Evaluate interactions with your QA rubric, compliance checklist, and business-specific scoring logic.
- ✓Surface evidence for coaching, compliance review, customer-care improvement, and AI-agent design.
Supervisor Operations ConsoleToday · Sales, support, compliance
QA running
Calls analyzed18,420
Compliance pass87%
Coachable moments312
AI-ready flows11
Agent confirmed customer intent, summarized next steps, and closed the loop.
Best practiceRequired pricing disclosure appeared after the offer instead of before it.
Review
Business case estimator
Build your QA coverage case in under a minute.
Move the sliders to enter your own contact-center numbers and instantly compare today’s manual QA sampling with AI-assisted supervision.
Adjust the values to estimate coverage expansion, review workload, and the operational impact of moving toward continuous quality assurance.
Estimated daily impact
15,800
additional customer interactions reviewed per day versus your current manual QA sample.
Manual reviews today200
QA Supervisor reviews16,000
Coverage lift80.0×
Manual hours avoided for equivalent coverage2,107
AI-readiness without guessing
From human QA to AI deployment confidence.
These sample scores illustrate how QA Supervisor can translate real contact-center interactions into AI-readiness indicators. Your organization’s dashboard will reflect your own conversations, campaigns, escalation policies, compliance requirements, and QA rubric.
Script clarity82
Knowledge coverage74
Escalation rules71
QA benchmark64
| Readiness area | What QA Supervisor looks for | Operational output |
|---|---|---|
| Predictable flows | High-volume intents with consistent resolution paths. | Candidate AI-agent call flows. |
| Human best practices | Winning phrases, rebuttals, summaries, and next-step setting. | Script and prompt guidance. |
| Risk moments | Disclosures, consent, escalation triggers, and prohibited claims. | Guardrails and QA checks. |
| Knowledge gaps | Questions agents cannot answer consistently. | Knowledge-base backlog. |
| Human handoff | Calls where emotion, complexity, or policy requires escalation. | Human-in-the-loop routing rules. |
Who benefits
Clear value for every team responsible for customer interactions.
QA, compliance, operations, and AI leadership care about different outcomes. QA Supervisor gives each team a clear reason to participate in the pilot.
Increase review coverage without losing judgment
Prioritize exceptions, coach with exact evidence, and keep human supervisors focused on decisions instead of random sampling.
Detect required-language and process failures faster
Monitor disclosures, consent, escalation obligations, and other risk controls across a far broader call set.
See quality patterns across campaigns and vendors
Compare teams, queues, clients, campaigns, and date ranges using a consistent operating view.
Launch AI voice agents from proven call patterns
Convert top human interactions into scripts, guardrails, escalation criteria, and acceptance benchmarks.
Reality Border · IQSTEL Digital ecosystem
One supervision layer connected to a broader AI and telecom stack.
QA Supervisor is positioned as the intelligence layer between today’s human operations and tomorrow’s supervised AI-enabled customer communications.
IQSTEL
Telecom and technology infrastructure supporting global communications.
Reality Border
AI-services company building practical customer-communication intelligence.
Airweb
Website and phone AI agents for multilingual customer engagement.
IQ2Call
AI-first and hybrid call-center services with escalation and analytics.
IQ Cortex
Governed intelligence, orchestration, traceability, and policy control.
FAQ
Common questions before a pilot.
No. The strongest entry point is often human QA. QA Supervisor evaluates human interactions first, then uses those findings to support hybrid and AI-first operations.
Yes. QA Supervisor can ingest interactions through file-based or API-based intake, so you can start with historical archives and keep receiving new interactions on an ongoing basis—without replacing your current stack.
Yes. Rubrics, prompts, scoring categories, and thresholds are configurable by campaign, client, or queue. You are not limited to a single generic out-of-the-box model.
No. QA Supervisor broadens coverage and prioritizes evidence. Human managers remain responsible for policy, judgment, coaching, escalation, and final decisions.
Start with one campaign, one rubric, and a representative set of historical plus current recordings. Use the pilot to validate scoring quality, dashboard usefulness, and actionability.
Start with a real call sample
Request a QA Supervisor pilot conversation.
Bring your current QA sample rate, daily interaction volume, main compliance concerns, and one target campaign. The first conversation should validate whether QA Supervisor can produce board-level value quickly.
- ✓Review recording intake and segmentation.
- ✓Define the first QA rubric and evidence requirements.
- ✓Identify the fastest path from QA analytics to AI-readiness.
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