Ask the Expert: How Verint AI’s research powers the next generation of CX Automation

In a recent post here on the Verint blog, we discussed how implementing AI-powered solutions in the contact center shouldn’t be an experiment, but rather a calculated process based on proven results.

In order to take risk out of a CX Automation deployment, Verint has an extensive record of AI research. This work is aimed at helping companies get the most out of their AI investment while also pushing this still-evolving technology forward.

To further understand Verint Da Vinci Labs’ research processes, we sat down with Dr. Ian Beaver, PhD, Verint’s Chief Data Scientist. In this conversation, he cuts through some of the AI noise explains how Verint turns AI research into positive AI outcomes.

What is the purpose of the Da Vinci Research team?

The Da Vinci Research team exists to accelerate AI innovations into Verint’s product suite in ways that directly benefit customers.  In practice, the team’s work feeds into the broader Da Vinci AI ecosystem — from the AI services layer to the products and workflows our customer use, where research-driven models are assembled into orchestrated workflows and deployed as specialized bots. The research team is, in essence, the origin point of the research-to-product pipeline that makes the entire Verint CX Automation Platform possible.

Why is it important for a company like Verint to drive research from within?

Verint operates in the customer engagement space — a domain with highly specialized challenges like noisy contact center audio, complex intent recognition, workforce demand forecasting, and multi-channel interaction analytics.

Off-the-shelf commercial AI models are not purpose-built for these problems. By driving research internally, Verint can develop proprietary AI engines that are trained on its unique asset: over two decades of customer engagement data, accessed securely through the Verint AI Lab in a SOC2, HIPAA, GDPR, and PCI-compliant environment.

Having deep internal research expertise is what allows Verint to make informed decisions about when its own models outperform commercial alternatives, and to rapidly integrate or replace external models as the landscape shifts — all while maintaining governance, compliance, and content guardrails through the platform’s built-in controls.

How does your research make for better Verint products?

The Verint Platform architecture is designed to maximize the impact of research. The Model Manager handles the full lifecycle of AI models — from training through deployment and monitoring. This means a model developed by the research team doesn’t just stay in a lab; it moves through standardized inference and deployment pipelines into production, with built-in logging and monitoring to ensure ongoing quality.

We’re able to translate research into practical tools through Prompt Management and Workflow Orchestration. Researchers and product teams can build, test, tune, and deploy AI services using generative AI, and chain multiple AI/machine-learning services together into complex workflows that become deployable bots. This is how, for example, a better intent discovery model or a more accurate transcription engine becomes a working product that customers can interact with daily.

We’re hearing more and more about AI governance and compliance. What are your teams doing in respect to that?

The Governance layer of our Da Vinci engine ensures that research-to-product delivery happens responsibly. Every AI service is subject to legal compliance review, regulatory compliance (PCI, SOC2, HIPAA, HITRUST, EU AI Act), content guardrails (covering toxicity, prompt injection risk, threats, and more), cost metering, and model monitoring through inference log analysis. This means research innovations don’t just make products more capable — they make them more capable within a framework that customers and regulators can trust.

What’s the best part of your job as an AI researcher?

The best part is the rare combination of depth and impact. At Verint, research isn’t an abstract exercise — there’s a direct, visible line from what we investigate in the lab to what thousands of organizations use every day to serve their customers. When we develop a new approach to intent discovery, improve transcription accuracy for noisy contact center calls, or build a better time-series forecasting model for workforce optimization, that work doesn’t stay in a paper — it moves through the Da Vinci platform’s Model Manager, gets assembled as a bot, and ships as a capability that real people rely on. That closed loop from research to product to measurable customer outcome is incredibly rewarding.

How important is access to data when it comes to AI research?

Verint has accumulated over two decades of customer engagement data — interaction data, experience data, workforce performance data — all accessible through the Verint AI Lab in a secure, PCI-compliant environment purpose-built for research. For an AI researcher, having access to that volume and variety of real-world data is an extraordinary advantage. It means we can train and validate models on the kinds of messy, authentic conversations that actually matter, not just clean benchmark datasets.

How does your research team interact with other teams at Verint?

It’s definitely a collaborative culture. The Da Vinci research framework is explicitly designed to foster collaboration across Verint’s AI/ML community, and the patent portfolio and publication track record reflect that. Being part of a team where researchers hold hundreds of patents and regularly contribute to leading scholarly and industry publications — while still shipping product — is the kind of environment where you can grow as a scientist and as a builder at the same time.

Read recent Verint research here.