
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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BAND develops comprehensive interaction frameworks tailored for large-scale applications of distributed AI agents. This platform enables real-time, collaborative communication between agents and humans while integrating a runtime control plane that maintains policy adherence, establishes authority boundaries, and guarantees transparency across varied systems.
Moreover, BAND supports developers, engineering teams, and leaders overseeing enterprise platforms that manage multi-agent ecosystems across internal frameworks, SaaS offerings, and collaborative environments with partners. This robust support not only improves operational efficiency but also stimulates innovation within intricate organizational frameworks, ultimately driving progress and adaptability in a rapidly evolving technological landscape.
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Moderation API
Moderation API helps online platforms protect their users and their brand with frontier AI moderation models built and trained in-house. Our models learn from a large proprietary dataset of real-world harmful content, so they deliver accuracy that generic AI tools can't match while keeping costs predictable at scale.
Automatically detect and act on toxicity, harassment, hate speech, sexual content, spam, scams, phishing, malicious links and personal data in text, images and other user content. Moderation API is built to catch the tricks bad actors use to evade filters, keeping communities, marketplaces, apps and AI products safe.
Moderation API adapts to your business. Train custom models on your own policies in Model studio, or deploy AI agents that apply your community guidelines with full context. Every piece of content gets a clear allow, review or reject recommendation, so your team only spends time where it matters.
A built-in moderation dashboard and review queues give your team full control. Review edge cases, take action against repeat offenders and continuously improve accuracy with every decision. Roll out safely with dry-run mode, testing on live traffic before anything is blocked.
Fast to deploy through SDKs, a REST API or no-code integrations. GDPR and CCPA compliant. Start free with no credit card.
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Amazon Rekognition
Amazon Rekognition streamlines the process of incorporating image and video analysis into applications by leveraging robust, scalable deep learning technologies, which require no prior machine learning expertise from users. This advanced tool is capable of detecting a wide array of elements, including objects, people, text, scenes, and activities in both images and videos, as well as identifying inappropriate content. Additionally, it provides accurate facial analysis and search capabilities, making it suitable for various applications such as user authentication, crowd surveillance, and enhancing public safety measures.
Furthermore, the Amazon Rekognition Custom Labels feature empowers businesses to identify specific objects and scenes in images that align with their unique operational needs. For example, a company could design a model to recognize distinct machine parts on an assembly line or monitor plant health effectively. One of the standout features of Amazon Rekognition Custom Labels is its ability to manage the intricacies of model development, allowing users with no machine learning background to successfully implement this technology. This accessibility broadens the potential for diverse industries to leverage the advantages of image analysis while avoiding the steep learning curve typically linked to machine learning processes. As a result, organizations can innovate and optimize their operations with greater ease and efficiency.
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