
Most contact centers are stitched together from tools that don't talk to each other — a phone system here, a chatbot there, a support queue that loses context the moment it changes hands. Dialpad Contact Center replaces that patchwork with one AI-native platform where voice, digital, and human agents work from the same intelligence.
The difference is agentic action. Rather than summarizing a call after the fact, Dialpad's AI agents reason through the issue in real time and carry it to resolution on their own — no handoff required unless one actually adds value. Voice and data stop living in separate silos, so every channel feeds the same connected picture of the customer.
That connected picture gets smarter with use. Dialpad is already past 775 million AI recaps, and every conversation adds to a base of intelligence that keeps improving resolution speed, agent output, and customer satisfaction over time. It's all run through Dialpad's Guardian layer, which keeps AI behavior secure, auditable, and within the boundaries enterprises expect.
The result: up to 80% of tickets resolved without a person touching them, and a support team that spends its time on the cases that actually need human judgment — intelligence doing the routine work, people handling what matters.
Skeptical an AI contact center can deliver on that? Dialpad's Proving Ground lets you pilot and measure real ROI before you commit, rather than adopting on promises alone.
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An API driven by Google's AI capabilities enables precise transformation of spoken language into written text. This technology enhances your content with accurate captions, improves the user experience through voice-activated features, and provides valuable analysis of customer interactions that can lead to better service. Utilizing cutting-edge algorithms from Google's deep learning neural networks, this automatic speech recognition (ASR) system stands out as one of the most sophisticated available. The Speech-to-Text service supports a variety of applications, allowing for the creation, management, and customization of tailored resources. You have the flexibility to implement speech recognition solutions wherever needed, whether in the cloud via the API or on-premises with Speech-to-Text O-Prem. Additionally, it offers the ability to customize the recognition process to accommodate industry-specific jargon or uncommon vocabulary. The system also automates the conversion of spoken figures into addresses, years, and currencies. With an intuitive user interface, experimenting with your speech audio becomes a seamless process, opening up new possibilities for innovation and efficiency. This robust tool invites users to explore its capabilities and integrate them into their projects with ease.
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Good Vibrations Company (GVC)
In multiple applications of GVC, the first step is to identify emotions: users speak for a few seconds, allowing the GVC Emotion Recognition algorithm to analyze various acoustic features of their voice to gain insight into their emotional state. The results generated by our emotion recognition system can subsequently inform other algorithms to determine appropriate responses for the user. At GVC, we prioritize feedback types that improve both user performance and overall quality of life, which involves examining signals from the user’s voice, heart, lungs, and other physiological systems. The GVC concept has been implemented across a variety of demonstration applications, all using a set of proprietary algorithms designed to evaluate different elements of the user’s speech, such as the GVC Emotion Recognition and GVC Voice Disorder Detection algorithms. Through this integration, we aim to enhance the user experience by ensuring that the responses are not only timely but also tailored to their emotional needs. By leveraging cutting-edge technology, we work to cultivate a more profound connection between users' emotional states and the feedback that they receive, thereby enriching their interactions. This holistic approach ultimately fosters an environment where users feel understood and supported.
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Google Cloud Vision AI
Utilize the capabilities of AutoML Vision or take advantage of pre-trained models from the Vision API to draw valuable insights from images stored either in the cloud or on edge devices, enabling functionalities like emotion recognition, text analysis, and beyond. Google Cloud offers two sophisticated computer vision options that harness machine learning to ensure high prediction accuracy in image evaluation. You can easily create customized machine learning models by uploading your images and utilizing AutoML Vision's user-friendly graphical interface for training and refining these models to achieve the best performance in terms of accuracy, speed, and efficiency. After achieving the desired results, these models can be exported effortlessly for deployment in cloud applications or across a range of edge devices. Furthermore, Google Cloud's Vision API provides access to powerful pre-trained machine learning models through REST and RPC APIs, allowing you to label images, classify them into millions of established categories, detect objects and faces, interpret both printed and handwritten text, and enhance your image database with detailed metadata for improved insights. This ensemble of tools not only streamlines the image analysis workflow but also equips enterprises with the means to make informed, data-driven choices more efficiently, fostering innovation and enhancing overall performance. Ultimately, by leveraging these advanced technologies, businesses can unlock new opportunities for growth and transformation within their operations.
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