QEval
Contact center QA teams evaluate 1 to 5% of calls manually. QEval eliminates that bottleneck by applying AI speech analytics and automated scoring to 100% of interactions across voice, chat, and email, using a classification engine trained on 138M+ real conversations.
Capabilities span quality monitoring, compliance detection for PCI, HIPAA, and GDPR at 98% accuracy, sentiment analysis, keyword identification, agent coaching workflows, performance gamification, and predictive analytics across 110+ configurable dashboards. Quality scoring runs at 94% accuracy with zero manual intervention.
Deployment takes 30 days. Industry standard is 90 to 120. No disruption to live operations. Etech Global Services built QEval from two decades of running Fortune 500 contact centers in healthcare, telecom, retail, banking, and BPO. ISO 27001, SOC 2, PCI-DSS certified. Built for QA leaders and operations teams scaling coverage without adding headcount.
QEval also provides call recording management, screen capture, custom evaluation forms, calibration tools for QA consistency, root cause analysis, trend identification, and automated alert systems for compliance breaches. The voice of customer module tracks customer sentiment across touchpoints to identify service gaps and training opportunities. Real-time monitoring lets supervisors intervene during live interactions. Role-based access controls, audit trails, and data encryption ensure enterprise-grade security. QEval supports multi-site and multilingual contact center environments with centralized reporting across locations.
API integrations connect QEval with existing CRM, telephony, and workforce management systems. Automated report scheduling delivers insights to stakeholders without manual effort.
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Google Cloud Speech-to-Text
An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.
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AssemblyAI
Transform audio and video files, along with live audio streams, into text effortlessly using AssemblyAI's robust speech-to-text APIs. Enhance your audio intelligence capabilities through features such as summarization, content moderation, and topic detection, all driven by state-of-the-art AI technology. AssemblyAI is dedicated to delivering an exceptional experience for developers, offering everything from thorough tutorials and detailed changelogs to extensive documentation. With a focus on core speech-to-text functionality and sentiment analysis, our straightforward API provides a comprehensive range of solutions tailored to meet the speech-to-text requirements of any business. We cater to startups at various stages, from those just starting out to those in the growth phase, by offering affordable speech-to-text options. Our infrastructure is designed to scale efficiently; we handle millions of audio files daily for a diverse clientele, which includes numerous Fortune 500 companies. By utilizing Universal-2, our most sophisticated speech-to-text model, you can capture the nuances of human speech, resulting in more precise audio data that generates clearer insights. This commitment to accuracy and efficiency makes AssemblyAI a leading choice for organizations seeking to leverage audio data effectively.
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Amazon Transcribe
Amazon Transcribe simplifies the integration of speech-to-text features for developers looking to enhance their applications. Analyzing and searching audio data presents significant challenges for computers, making it essential to convert spoken words into written format for effective usage in various applications. Traditionally, businesses had to collaborate with transcription services that imposed costly contracts and were complicated to integrate with existing technology, making the transcription process cumbersome. Moreover, many of these services relied on outdated technologies that struggled to handle specific situations, such as the low-quality audio typical in contact center environments, leading to decreased accuracy. In contrast, Amazon Transcribe utilizes an advanced deep learning technique known as automatic speech recognition (ASR) to convert speech into text efficiently and with high precision. This service is versatile, allowing for the transcription of customer service interactions, the automation of subtitling, and the creation of metadata for media files, ultimately resulting in a comprehensive and searchable archive of content. With its user-friendly design and robust capabilities, Amazon Transcribe stands out as an essential tool for developers aiming to enhance the functionality of their applications.
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