Auto-QA for contact centers

Auto-QA every conversation

Automate compliance checks and evaluate every call, chat and ticket against your own measures of success with the power of EvaluAgent and Artificial Intelligence combined.

  • 100% Coverage

    Augment existing QA processes with thousands more evaluations automatically completed to prioritize coaching & training.

  • Monitor any contact channel

    Every conversation counts; whether it’s a call, email, live chat or ticket. Unlike the competition, we cover EVERY channel.

  • Make Analytics Actionable

    Our automated workflows operationalize your insights to ensure they are actionable and drive performance improvement.

  • Unlimited usage. One fixed price.

    Avoid unpredictable pricing models. Pay a fixed price, per user, per month.

Reach new levels of operational excellence with Auto-QA

Mitigate risk, reduce operating costs and deliver great customer experiences consistently with the power of AI.

Call Center Quality Assurance

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Customer stories

Throughout Europe, North America and Asia-Pacific.

FAQs

What is Auto-QA?

Auto-QA leverages AI and machine learning to automatically score and identify high-risk conversations by analysing every call, chat and ticket. The resulting insight from across your entire sales and service operation is then automatically grouped and filtered to prioritize deeper dive manual QA, coaching and training on the agents most in need of help.

Can I auto-QA conversations across every channel?

Yes. Unlike other solutions on the market, EvaluAgent Auto-QA can be activated across calls, tickets, social media and chat interactions. To make things as easy as possible, we’ve set up lots of integrations with many of the leading call and ticket management platforms so that any type of conversation can be imported into EvaluAgent.

How does Auto-QA enhance my existing approach to quality management?

It’s important to note that Auto-QA doesn’t remove the need for manual QA. Instead, AI-fuelled automation augments and super-charges QA by running automated quality checks across every conversation to help you dramatically increase your QA-coverage and reduce risk by laser-focusing and thus improving the efficiency and effectiveness of your QA team. So, in effect, it delivers more coverage, less risk, more efficiency, less cost.

How does EvaluAgent’s redaction help keep personal and sensitive data safe and secure?

In addition to our enterprise-level security standards, EvaluAgent’s redaction feature ensures you can totally remove sensitive or customer identifiable information (CII) from any ticket-text, call-transcription or audio-file stored within EvaluAgent without losing context.

Can agents view Auto-QA results?

You decide by simply configuring the platform to control who has access to auto-QA results. For example, whilst you’re testing and validating your auto-QA scorecards, you may choose not to reveal results to the operation until you’re confident with EvaluAgent’s scoring accuracy.​

How accurate is the transcription?

The transcription will be good enough to read and understand without listening to the audio, but no transcriptions service on the market is 100% accurate and it doesn’t need to be. There will be words and phrases that are specific to your organisation such as product names or brands that the system will not be able to recognise ‘out of the box’. There will also be certain speakers that are picked up more effectively than others. The impact of background noise will also mean that everyday words can be missed.

All of these affect transcription accuracy, but many can be addressed over time as you tune both the transcription process and write better queries as we discuss in our popular blog: Why Transcription Accuracy Doesn’t Matter As Much As You May Think.

What is Speech Analytics and what is Text Analytics?

Speech analytics is one component of Auto-QA specifically when applied to calls. Firstly, the Automated Speech Recognition (ASR) engine accurately transcribes call recordings into text. That text is subsequently mined using the aptly named text analytics engine to find words and phrases which, if found, imply something may have happened. However, secondly, the ASR engine also detects and analyses audio-metrics such as a speaker’s pace, pitch and tone as well as silence time and over-talk. These audio metrics, alongside the text analytics results can be combined to reveal actionable insights.

Further reading

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