Coval Raises $28M in Series A Funding Led by Norwest 

The round also saw participation from Base10 Partners, Twilio Ventures, and Y Combinator. The raise brought the total capital to $31 million since its launch in 2024.

The company plans to use the funding to expand its operations and further develop its products and technology.

Coval is a simulation, observability, and labeling platform for AI voice and chat agents. It helps enterprises test, monitor, and improve AI agents at scale.

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As Fortune 500 companies adopt voice AI and explore new ways to use it in their businesses, Coval provides the infrastructure needed to deploy these systems reliably and with confidence.

Coval performs tens of millions of AI agent evaluations. With the new funding, the company plans to grow its sales and solutions engineering teams to meet increasing customer demand.

The funding will also support product development, including more advanced simulation tools, additional integrations, and improved human review and monitoring capabilities.

Unlike many evaluation tools that are designed mainly for developers, Coval helps teams across operations, quality assurance, engineering, and product work together to build and scale AI agents.

The platform covers the entire voice agent lifecycle, including pre-deployment simulation, live monitoring, human review, and performance evaluation. It is specifically built for voice AI, with features for audio processing, call quality analysis, telephony latency monitoring, transcription error detection, and workflow evaluation.

Autonomous systems inspire Coval’s approach. Drawing on experience from Waymo, the company applies a simulation-first methodology to voice AI. As self driving cars rely on perception, planning, and control systems, voice agents use transcription, language models, and text to speech systems. In both cases simulation plays a critical role in testing and improving performance before deployment.

"Reliability and observability are a top priority for us at Zoom as voice AI moves into customer-facing production environments," said Ram Rajagopalan, Head of Product – CX AI at Zoom. "Coval gives Zoom's customers the ability to evaluate conversations systematically at scale, identify edge cases before they impact users, and move significantly faster with confidence."

"Voice agents introduce a new level of complexity compared to traditional software testing," said Anoop Dawar, COO at Deepgram. "Brooke has built Coval into a core part of the modern enterprise's evaluation stack by improving reliability before scaled deployment. For any serious enterprise deployment, this is no longer a nice-to-have. At Deepgram, we power the voice AI infrastructure teams build on, but thanks to our partnership with Coval, enterprises can rest assured it's working properly."

"Twilio's open, flexible, and model-agnostic infrastructure for the agentic era is driving the shift toward human-like voice AI agent experiences becoming the norm in customer engagement," said Andy O'Dower, VP, Field CTO at Twilio. "Trust is critical to scaling these experiences, and our investment in Coval reflects our conviction that comprehensive evaluation and testing tools, combined with a strong observability and reliability layer, are foundational to maintaining momentum in today's voice AI renaissance."

"Voice is going to be the number one interface for how humans interact with AI, and that shift creates an entirely new infrastructure layer for enterprises," said Scott Beechuk, partner at Norwest. "With her deep experience building evaluation systems for autonomous technologies at Waymo, Brooke is uniquely positioned to lead Coval in defining how companies deploy and scale voice agents reliably. She helped prove self-driving cars could work, and now she's tackling voice AI."

About Coval

Founded in 2024 by Brooke Hopkins, Coval is a platform that helps enterprises test, monitor, evaluate, and improve AI voice agents at scale. Brooke Hopkins previously led evaluation infrastructure at Waymo and Coval applies the same simulation first approach used in autonomous vehicles to make AI voice agents more reliable and effective in production.

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