AI-Powered Call Auditing in Contact Centers: Better Quality, Faster Insights

Is your quality assurance team reviewing fewer than 2% of your total call volume? 

This is the industry standard - and it MISSES 98% of potential risks…

Which is why tips based on random sampling cause agents to feel judged unfairly.

Meaning most coaching fails to stick.

This guide explains how real-time call quality monitoring delivers full visibility and faster insights.

Using AI-powered call auditing changes your contact center from a cost burden into a valuable business asset.

Why Manual and Rule-Based QA Cannot Grow

Manual QA grows directly with staff numbers which means you must hire more analysts to match call volume increases.

Most companies accept a standard where supervisors review only a few calls. This method misses systemic problems and churn risks because the sample size remains too small.

Manual auditing is also subjective. Auditor fatigue or bias influences scores. This inconsistency frustrates agents who feel their performance review depends on chance.

Real-Time vs Post-Call Detection

Manual limits become clear regarding timing. Traditional auditing identifies problems days after the customer leaves.

Real-Time Detection monitors the interaction while it happens. If a customer shows distress using real-time call quality monitoring can help you alert a supervisor to enter the call or give the agent immediate suggestions guided by past interactions.

How Thunai Works with Genesys for Automated Audits

Many contact centers using Genesys face limits with built-in reporting tools. Thunai connects a specialized AI layer with your current software for automated call quality monitoring in real-time.

Thunai uses a central intelligence called Thunai Brain. This system connects to your telephony provider or CCaaS software like Genesys via the Thunai MCP Multi-Connect Protocol.

Here is how Thunai changes the auditing process within a Genesys environment:

• Universal Ingestion: Through the MCP Thunai links with 35+ enterprise apps. It collects recording data and customer history into one unified knowledge graph.
  
• Thunai Omni Analysis: After the system collects data the Thunai Omni module performs the first analysis layer on voice and chat. It tracks sentiment instantly so the system scans 100% of calls
  
• Contextual Scoring: Thunai Brain interprets context unlike simple keyword matchers. It solves contradictions across documents to score calls based on intent.
  
• Actionable Data Sync: The MCP allows two-way data movement. Thunai updates the CRM or ticketing system with audit results automatically.

The Advantages of Moving to AI-Powered Call Auditing

Moving to an AI model brings immediate operational gains. It changes the contact center from a data void into a source of insights. Here are five main advantages:

1. Lower QA Cost Without Lowering Quality

The economic argument for automated call quality monitoring is clear. AI-powered call auditing changes the link between headcount and coverage:

• Unlimited Growth: Coverage grows with cloud compute power instead of human labor. You can audit 1000 calls for nearly the same effort as auditing 10.
  
• Lower ACW: Automated summaries cut After-Call Work ACW by 2 to 4 minutes per interaction.
  
• Higher Output: Agents spend less time typing notes and assist more customers. This increases workforce output without hiring new employees.

2. Multi-Language and Accent Handling

Contact centers contain noise and diversity. Global teams often face accent bias when humans score calls - which is why AI-powered call auditing can help bridge this gap. 

• Advanced Acoustic Models: Thunai uses models that process background noise and separate speakers even in difficult audio settings.
  
• Standardized Scoring: AI standardizes the listening process. The system scores an agent in Manila with the same criteria as an agent in Manchester.
  
• Removing Bias: This removes the accent penalty common with human auditors and supports fair grading everywhere.

3. Better Coaching and Compliance with Insights for Impact

The goal of AI-powered call auditing is better performance rather than just assigning a score. AI changes the supervisor role from data gathering to coaching.

• Active Monitoring: Thunai Reflect monitors product and agent health constantly.
  
• Directed Coaching: Supervisors no longer spend weeks listening to random calls to find one teaching moment. Thunai collects insights automatically.
  
• Closed-Loop Feedback: The system turns insights into tickets for product and engineering teams. This sends feedback to the people building the product.

4. Escalation and Repeat-Call Prevention

Repeat calls drive up costs. AI auditing stops escalations before they occur.

• Topic Modeling: AI auditing groups calls into topics without prior training. Managers find unknown reasons for contact spikes like a website error.
  
• Live Sentiment Analysis: Thunai Omni uses live sentiment analysis to flag frustrated customers immediately.
  
• Churn Prevention: This active method saves calls before the customer hangs up in anger. It lowers churn risks that manual QA misses.

5. Objective Bias-Free Scoring on All Calls

Human opinion hurts fairness. AI call scoring applies the same standards to every interaction.

• Contextual Understanding: AI call scoring supports fairness by using its Brain to understand the full interaction context instead of just spotting keywords.
  
• Set Parameters: The Thunai Meeting Assistant scores calls based on set organizational rules.
  
• Building Trust: When agents know the system evaluates 100% of their work fairly they accept feedback and participate in coaching.

AI Call Scoring and Sentiment Analysis in Action in Different Industries

Sentiment analysis measures more than happiness or sadness. Using AI-powered call auditing, you can track the tone and pitch alongside words to understand the emotional state of a conversation. Different industries use this data in unique ways.

I. Financial Services: Compliance as a Safety Net

Banks face heavy fines when agents miss mandatory scripts like the Mini-Miranda disclosure. Manual checks fail to catch these rare errors because the sample size is too small - which is where AI-powered call auditing can be invaluable.

• Compliance Shield: Thunai AI call scoring flags calls missing specific phrases instantly to stop regulatory penalties.
  
• Data Safety: The system redacts sensitive numbers like credit card details automatically to keep customer data safe.

II. Healthcare: Empathy and Privacy

Providers work hard to balance patient care with strict privacy laws like HIPAA. Using AI-powered call auditing, you can help your agents show better compassion while they protect sensitive medical records during every conversation.

• Automatic Redaction: Thunai scrubs Protected Health Information PHI from transcripts to stop privacy breaches.
  
• Empathy Scoring: The system analyzes active listening patterns to find agents who need support due to compassion fatigue.

III. Sales and Revenue: Spot More Sales Opportunities

Revenue teams use sentiment analysis to find hidden sales opportunities during service calls. AI-powered call auditing turns standard support interactions into profitable lead generation channels.

• Deal Detection: Thunai Revenue AI captures buying signals like competitor mentions that tired agents often miss.
  
• Opportunity Scoring: AI call scoring systems can grade calls based on intent indicators to direct the sales team toward high-value leads.

IV. Logistics and Transportation: Speed and Dispute Resolution

Speed matters in logistics. Agents face high call volumes regarding package status or disputes. Manual auditing fails to keep pace with daily micro-interactions that AI call scoring can measure without fail.

• Fast Summaries: using AI-powered call auditing allows you to transcribe and summarise these fast calls instantly. It captures tracking numbers and dispute details without manual typing.
  
• Trend Identification: The system detects patterns such as a surge in calls from a specific area. Using AI-powered call auditing in real-time also helps warns operations about potential delays before they become larger problems.

V. Legal Services and Law Practices: Accuracy and Liability

Accuracy is mandatory for law firms. Manual note-taking during intake or deposition prep contains errors and distracts from legal analysis.

• Central Knowledge Base: Thunai Brain acts as a secure searchable storage for all case-related audio and documents. Attorneys use Ask Thunai to retrieve specific details from past calls.
  
• Confidentiality: Thunai separates data to isolate sensitive information. Firms maintain privilege while using AI for transcripts and action items.

Moving to Real-Time Monitoring with Thunai

The future of quality assurance happens in real time. Waiting for a post-call report fails when customer loyalty disappears in seconds.

Which is why tools like Thunai Omni handle the requirement for AI-powered call auditing. With real-time call quality monitoring, you get live transcription and sentiment tracking while the call progresses allowing for a newer, more proactive management style.

Supervisors view a live dashboard of all active calls marked by sentiment.

If a call turns negative the supervisor can easily enter the call to assist or even the agent can receive immediate suggestions completely from Thunai AI’s past call transcripts, tickets and database.

Want to see how AI call scoring works in real-time? Why not see Thunai in action!

FAQs on Automated Call Monitoring for Contact Centers

How do you measure call quality?

You measure call quality by comparing an interaction against specific standards. Modern AI scores 100% of calls based on script usage tone compliance and intent resolution. Platforms like Thunai automate this by comparing the conversation against organizational rules.

What is quality monitoring in BPO?

Quality Monitoring QM in outsourcing tracks agent performance to meet client agreements. It is shifting toward automated Auto-QA to handle massive interaction volumes. This balances speed with customer experience.

What are the 7 types of monitoring?

The types of monitoring include Knowledge monitoring that checks if agents find correct data, Omnichannel monitoring tracks quality across voice and chat and Workflow monitoring verifies that agents follow set steps. Aside from this, there is also Data monitoring that checks accuracy between systems, product health monitoring tracks how software issues affect calls, Revenue monitoring grades calls based on sales results and Interaction monitoring checks active listening during live meetings

What are the 5 key performance indicators of a contact center?

AI auditing measures five metrics: First Contact Resolution improves by finding root causes, while Average Handle Time drops via automation. Real-time tracking boosts Customer Satisfaction, QA Scores become factual, and fair tools improve Agent Retention