
Customer experience shouldn't run on disconnected tools and static scripts. Dialpad Contact Center brings voice, digital channels, and human agents together in a single AI-native platform, built to act — not just record — on every customer interaction.
This is Agentic AI in practice: agents that reason through a problem, take the next step, and drive it to resolution without waiting on a human to intervene. Where legacy systems leave data trapped in silos, Dialpad Contact Center closes that gap, linking voice and data so context travels with the customer instead of getting lost between systems.
The payoff compounds. Dialpad has already generated over 775 million AI recaps, and each new interaction adds to a growing base of operational intelligence — sharper resolution paths, more productive agents, better outcomes quarter over quarter. None of it runs unchecked: Dialpad's Guardian layer keeps AI operations secure and governed, so intelligence scales without sacrificing oversight.
In practice, that means up to 80% of issues get resolved autonomously, freeing your team to focus on the conversations that genuinely need a human. Intelligence works at the edge; people stay at the center of the experience.
And you don't have to take the ROI on faith. Through Dialpad's Proving Ground, enterprises can validate performance and cost savings before rolling out at scale — a far more reliable path than betting on a brittle, rules-based bot.
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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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Affectiva
Affectiva, a leader in Emotion AI technology, is now part of the Smart Eye group, continuing to revolutionize how machines understand human emotions and cognitive states. Founded by Dr. Rana el Kaliouby and Dr. Rosalind Picard, Affectiva’s technology is applied in industries like media analytics and automotive, where it helps companies understand audience engagement and improve vehicle safety systems. The company's AI uses machine learning and computer vision to detect nuanced emotions and interactions, offering deep insights into human behavior. Affectiva has received numerous accolades, including recognition in the CB Insights AI 100 and Forbes AI 50, and continues to innovate in the field of ethical AI development.
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Good Vibrations Company (GVC)
In various GVC applications, the initial phase involves recognizing emotions: the user vocalizes for several seconds, and the GVC Emotion Recognition algorithm evaluates numerous acoustic characteristics of their voice to derive an understanding of their emotional condition. The outcomes from our emotion recognition system can then be utilized by other algorithms to select suitable responses for the user. At GVC, our main focus is on types of feedback that enhance the user's performance and overall quality of life. This includes analyzing signals from the user's voice, heart, lungs, and other bodily organs. The GVC concept has been put into practice in a range of demonstration applications. These applications utilize a collection of proprietary algorithms that assess various aspects of the user's speech, including the GVC Emotion Recognition and GVC Voice Disorder Detection algorithms, ultimately aiming to create a more responsive and supportive user experience. By integrating advanced technology, we strive to foster a deeper connection between the user's emotional state and the feedback provided.
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