New transcription services

Seamless transcription solutions for enhanced QA

Accurate, affordable, and reliable transcriptions are making comprehensive omni-channel Auto QA possible

Request a demo
Trusted by 100K+ team members around the globe
Transcriptions

Accurate transcription for omni-channel QA

MaestroQA offers an affordable solution for companies, transcribing 100% of calls without high expenses, unlike others who charge per minute or hour.

Diarize conversations to distinguish speakers and gain insights for enhancing agent performance and customer service
Redaction protects customer data by removing sensitive information
Interactive transcript UI enables efficient grading by skipping dead time and identifying friction points
Set custom vocabulary to catch industry-specific words and  boost accuracy
Easily report hold time or escalations for analysis
Auto QA

Leverage the power of AI

Utilize AI classifiers to build custom logic that highlights customer interactions directly impacting your business goals.

Build custom Quality KPIs for comprehensive performance assessment
Identify areas for targeted QA initiatives
Automatically grade 100% of interactions for increased efficiency

How AI is Transforming Quality Assurance in Customer Support

Download the eBook

“We’re only sampling 2% of our interactions – AI can help us to do 100% sampling, providing us with more insight”

MaestroQA Customer
Export Conversations

Effortlessly export transcripts into your internal tools for AI projects

Gain a competitive edge by seamlessly accessing and leveraging call data from your Helpdesk and MaestroQA. This empowers improved decision-making and enhances efficiency across multiple projects, regardless of the tool you’re working in.

Enable seamless S3 integration for exports
Generate detailed CSV transcripts that include metadata
Access key information like speakers and timestamps for enhanced analysis
Learn More

Learn how Novo is Using Auto QA to Get to Root-Cause Faster & Prevent Customer Friction

Watch the Webinar