MaestroQA has AI! But is it built to check a box or make your life easier? Compare Loris and MaestroQA to understand who really delivers better QA and VOC insights.
“We have actually shaved a little over three minutes of AHT now that we don’t have the agent tag tickets anymore.
That has reduced our cost per case by 23%.”
Loris has a library of AI models proven on more than 500 million interactions, giving you years of data science expertise from day 1.
At-a-glance performance views on human and AI agents, so you see where to automate and where to empathize.
Highlight outlier agent performance and to policies you care about, so you can focus on your biggest issues more than your scorecard.
If you’re looking to do more than check the QA box, Loris is clear choice.
Quality Assurance Features |  |  |
|---|---|---|
AI-First Customer Service QA Platform | YES | NO |
Prebuilt, Proprietary AI Models Proven on 500M Conversations | YES | NO |
Organization-specific Contact Driver Model (Intent Classifier Built & Maintained by Loris) | YES | NO |
Ask Loris, Built-in AI Data Analyst (Instant Answers to Plain-Language Questions) | YES | NO |
Recognized Leader in AI & Customer Analytics | YES | NO |
Ability to Get Started in Weeks | YES | NO |
 | YES |
 | NO |
 | YES |
 | NO |
 | YES |
 | NO |
 | YES |
 | NO |
 | YES |
 | NO |
 | YES |
 | NO |
MaestroQA is one of the most well-known providers in the call center quality assurance software space. For more than 10 years, MaestroQA’s approach has been to digitize manual QA spreadsheets and paper-based processes to preserve much of the traditional QA process in its digital quality and coaching tool. In an attempt to keep pace with newer AI for QA providers like Loris, MaestroQA has added some basic user-defined prompting.
MaestroQA has a number of both traditional and emerging competitors in the quality assurance and customer analytics categories.
AI-first providers:
Offerings more similar to Maestro’s or focused on the SMB market:
Like other tools in this space, MaestroQA connects to your customer service platform and collects data on customer interactions, such as calls, chats, or emails. It then uses this data to create evaluations and scorecards for agents, helping businesses identify areas for improvement.
The main issue with MaestroQA historically was the need for extensive customization and lack of true automation. While this makes it effective for organizations who have the time and resources for a large, complex QA operation, it’s not as effective for organizations either using more chatbots/AI Agents or who want to streamline their quality programs.
MaestroQA’s new AI capabilities don’t seem to have addressed the ease-of-use issue, since they provide the user with the ability to use prompts to glean information from conversations, but aren’t providing any prebuilt analysis themselves. For organizations that don’t leverage the AI prompts, MaestroQA uses manual agent tagging to derive customer insights, which can be both subjective and labor intensive.
MaestroQA is best for organizations who want a traditional quality program. That includes dedicated QA managers, QA analysts, and even QA operations to define and refine quality workflows. This could also include organizations that want to have staff assigned to AI prompting of their conversations. This is a more human-driven approach, meaning that increases in agents may need an increase in supporting quality staff. Organizations choosing MaestroQA would be more focused on maintaining human reviewers in their quality feedback loop and less focused on deriving customer analytics. It is possible these organizations would need to leverage a separate Voice of the Customer (VOC) analytics tool.
