From 3% to 100%: How Diabolocom is Transforming Quality Monitoring in Contact Centers

Why Traditional QA Isn’t Working Anymore

Jonathan opened the segment with a reality check:

“Customer experience is really related to the survival of companies, but most teams spend a lot of money to analyse just 3% of their calls. That’s time-consuming, biased, and it leaves huge blind spots.”

QA has traditionally relied on supervisors manually reviewing transcripts or listening to a few selected calls. The result:

• Only around 3% of interactions reviewed
• Hours of supervisor labour for each batch
• Scoring that varies depending on who reviews it (and when)
• Feedback that arrives too late to make a difference

In fast-moving environments, these gaps can mean missed opportunities and lower customer satisfaction.

Inside the Diabolocom AI engine

So, what happens when a quality monitoring system is built for a generative AI model from day one? Jonathan outlined several key differences:

With this approach, Diabolocom helps teams monitor 100% of interactions, while turning feedback into action within minutes.

Interview highlight: A Smarter Way to Manage Change

When asked about the relationship between AI and human agents, Jonathan was clear: the goal isn’t to replace agents, but to support them:

Jonathan: “It’s more about bringing AI to existing workflows and then improving those workflows. Think of it as a personal coach. Agents get feedback every day, not once a month.”

By eliminating sampling bias and delivering clear, real-time insights, AI Assist boosts both performance and morale.

Results in Just Six Months

Diabolocom’s AI-first approach isn’t just theoretical. One recent European customer saw measurable improvements:

• 50% faster voicemail and telemarketing handling
  
• First contact resolution improved from 65% to 85%
  
• Net Promoter Score increased into the mid-70s (well above industry norms)

The rollout took just a few weeks, thanks to open APIs and secure, EU-based data hosting.

Four Ways AI is Reshaping Contact Center Quality

At a glance:

• Full coverage is now affordable—stop optimizing on 5 % of data.

You no longer need to rely on 5% of data to understand 100% of your performance. AI enables teams to review every customer interaction without increasing headcount.

• AI-first platforms outperform retrofitted add-ons.

Older QA tools with basic AI integrations struggle to keep up. Platforms built for AI from the start offer deeper insights, faster updates, and better long-term value.

• Closed-loop workflows turn scores into revenue-impacting actions.

Modern systems don’t just score calls, they drive next steps like coaching sessions, follow-ups, or real-time prompts while the customer is still engaged.

• Agent-centric feedback converts QA from policing to coaching.

Transparent, ongoing feedback helps agents improve continuously, turning quality monitoring into a driver of performance and engagement.

Summary

To summarise, quality monitoring is no longer about sampling, but seeing the full picture. With AI, contact centers can finally move beyond analyzing a tiny fraction of calls and start learning from every single interaction. This shift brings more data and better decisions, enabling teams to act on real insights instead of assumptions. By replacing outdated tools with platforms built specifically for AI, businesses gain faster updates, clearer visibility into customer intent, and smarter automation all without adding operational complexity.

Perhaps most importantly, AI-powered quality assurance changes how teams work. Instead of static reports or delayed feedback, agents receive clear, actionable input while it still matters. This turns QA from a reactive checklist into a proactive coaching tool, helping agents grow in real time, boosting customer satisfaction, and ultimately driving revenue.

When quality monitoring becomes a continuous improvement loop rather than a compliance exercise, everyone benefits.

Watch the full interview