Yes, MaestroQA finally has AI!
But does improve the quality of your experience, or just check your existing QA boxes faster? See why brands switch to Loris to see the big picture with agent and customer 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%.”
If you’re looking to do more than check the QA box, Loris is clear choice.
Quality Assurance Features |  |  |
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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 |
AI analytics are the first step to understanding issues before you try to solve them through QA, AI agents, or some other tool. That’s because AI Analytics helps you:
Loris AI delivers agent and customer insights from millions of conversations. Schedule a meeting to see it for yourself.
MaestroQA is an established software provider in the call center quality assurance software space. For more than 10 years, MaestroQA’s approach has essentially been to digitize traditional QA spreadsheets and manual processes. While this provided structure for distributed teams, it also preserved the slow and labor-intensive nature of traditional QA. In an attempt to keep pace with native AI for QA providers like Loris, MaestroQA has added some basic user-defined prompting through a LLM.
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 nascent 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 small to medium-sized organizations who want to preserve their 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. Organization using MaestroQA would be more focused on maintaining human reviewers in their quality feedback loop and less focused on deriving agent and customer analytics to see broader trends and systemic issues. It is possible these organizations would need to leverage a separate Voice of the Customer (VOC) analytics tool.
