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Users of this software have discovered that the audio graph feature greatly enhances their navigation capabilities, leading to increased …
Overview
What is Level AI?
Level AI, headquartered in Mountain View, offers contact center QA software powered by AI & NLU. Level AI helps quality assurance teams to automatically monitor and evaluate agent conversations. Additionally, Level AI's Real-time Agent Assist™ understands customer intent, according to the…
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What is Level AI?
Level AI, headquartered in Mountain View, offers contact center QA software powered by AI & NLU. Level AI helps quality assurance teams to automatically monitor and evaluate agent conversations. Additionally, Level AI's Real-time Agent Assist™ understands customer intent, according to the vendor…
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Product Details
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- Integrations
- Tech Details
What is Level AI?
Level AI offers contact center QA software powered by AI & NLU. Level AI helps quality assurance teams to automatically monitor and evaluate agent conversations. Additionally, Level AI's Real-time Agent Assist™ understands customer intent, according to the vendor with more accuracy than keyword-based systems and provides agents with guidance using the best resources for each scenario.
Quality Assurance
While keyword-based (NLP) systems can require constant updates and result in false positives, Level AI uses NLU to understand customer intent. The vendor states that provides 2X the accuracy, and minimal maintenance. Level AI’s semantic intelligence engine analyzes, scores and tags key moments in every agent conversation. And Level AI's Rubric Builder™ allows QA teams to create custom grading systems to evaluate all support conversations. Customize grading questions, categories, scores, weightage, and more for different user roles and support channels. And its InstaReview™ feature automatically picks out the most important conversations and tags them with color-coded labels for QA teams to identify.
Agent Assist
Quality Assurance
While keyword-based (NLP) systems can require constant updates and result in false positives, Level AI uses NLU to understand customer intent. The vendor states that provides 2X the accuracy, and minimal maintenance. Level AI’s semantic intelligence engine analyzes, scores and tags key moments in every agent conversation. And Level AI's Rubric Builder™ allows QA teams to create custom grading systems to evaluate all support conversations. Customize grading questions, categories, scores, weightage, and more for different user roles and support channels. And its InstaReview™ feature automatically picks out the most important conversations and tags them with color-coded labels for QA teams to identify.
Agent Assist
Real-Time Agent Assist™, from Level AI, helps to accelerate agent onboarding, surface 2X more accurate information in real-time and enable autonomous learning for support agents. The vendor states real-time Agent Assist™ understands customer intent with 2X the accuracy of keyword-based systems and provides agents with guidance using best resources for each scenario. Level AI enables autonomous learning for agents by evaluating every interaction across voice calls, email and chat. Agents can review their scores, keep track of their most important metrics, and learn how to improve performance without 1:1 human coaching. They can also train Level AI by providing feedback. Its models learn and provide better results over time. If Agent Assist™ doesn’t help with a customer issue, agents can flag it. Level AI integrates with a variety of knowledge bases.
Analytics
Level AI provides a unified view of everything happening across the contact center with native dashboards and custom reports for QA teams and agents. Level AI allows admins to build custom reports from both outside data sources and newly captured data in Level AI.
Features of Level AI's Analytics include:
Analytics
Level AI provides a unified view of everything happening across the contact center with native dashboards and custom reports for QA teams and agents. Level AI allows admins to build custom reports from both outside data sources and newly captured data in Level AI.
Features of Level AI's Analytics include:
- Omnichannel CX Insights - See what’s happening across voice calls, email, and chat conversations – from agent performance to QA scores of individual agents or teams.
- Uncover Critical Insights With Custom Reports - Generate custom reports, track important metrics, apply filters, group data, and more with Level AI’s Query Builder™.
- Keep Stakeholders On The Same Page - Level AI is focused around VoC (Voice of Customer) and can automatically deliver custom reports to contact center leaders, as well as other business teams, such as product and compliance.
- Integrate With Other Data Analytics Tools - Combine contact center data with preferred analytics and business intelligence tools. The Level AI API allows users to push large amounts of data to and from the platform.
Level AI Videos
Streamlining Quality Assurance with Level AI
Level AI: Agent Assist Product Demo
Level AI Integrations
Level AI Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
- Cons
Users of this software have discovered that the audio graph feature greatly enhances their navigation capabilities, leading to increased productivity. By providing a visual representation of conversations, users are able to easily locate specific points within recordings, saving valuable time and effort. Additionally, the software offers real-time conversation monitoring and AI assistance, empowering users to evaluate their interactions with clients effectively. This enables them to provide immediate feedback and support while identifying areas for improvement. However, it is worth noting that some users have reported that the user interface could benefit from improvements. Furthermore, the software has limitations in differentiating between real positive and negative conversations accurately.
One of the standout aspects of this software is its ability to provide 100% coverage of interactions, ensuring that no conversation goes unnoticed or unanalyzed. Users have praised this comprehensive approach as it allows for quality feedback for support agents. With access to all interactions, agents can identify strengths and weaknesses in their communication strategies and implement targeted improvements. The software also has a valuable role in QA analytics by summarizing the results of the QA score. By consolidating the data and highlighting areas of concern or success, it aids in making informed decisions and driving future improvements. In summary, key use cases of this product include real-time conversation monitoring, AI assistance, coverage and quality feedback for support agents, and robust QA analytics.
Helpful Call Details: Many users have found the call details feature to be extremely helpful in identifying areas of improvement in their customer interactions. This feature allows them to delve deep into the specifics of each call, enabling a thorough analysis and providing valuable insights for enhancing performance.
Efficient Audio Graph Navigation: Numerous reviewers have expressed their appreciation for the tool's easy-to-use audio graph navigation. This intuitive functionality enables users to quickly and effortlessly navigate through call recordings, saving time and streamlining the analysis process.
Convenient Real Time Quality Status: Users have praised the Real Time Quality Status feature for its convenience and usefulness. By providing an up-to-date snapshot of scorecards and quality metrics, it offers a seamless way to monitor performance in real time.
Unclear Call Sentiment Standards: Some users have expressed confusion about the standards for analyzing call sentiment, stating that it is not clear what criteria are used to classify calls as positive or negative. This lack of clarity has led to inaccurate numbers and affected the overall accuracy of the sentiment analysis.
Overwhelming Dashboard: Users have mentioned that the dashboard contains an excessive amount of information, making it difficult to navigate and find what they need for simple tasks. This overload of data can cause users to feel lost and unsure about where to go within the dashboard.
Lack of Easy Access to Case Numbers in Salesforce: One user suggested having a convenient way to view case numbers and details directly in Salesforce. This feature would streamline workflow and improve efficiency by eliminating the need for users to switch between different platforms when accessing case-related information.