VoiceRun has secured $5.5 million in seed funding as it moves to redefine how AI voice agents are built, tested, and deployed at scale. The new capital marks an early but significant vote of confidence in the startup’s vision to turn voice automation into a disciplined, code-first engineering process rather than a fragile collection of visual workflows.
The company was founded by Nicholas Leonard and Derek Caneja, who set out to build AI voice agents and quickly realized the market had a structural problem. Many existing tools allowed teams to ship quickly, yet the final products often struggled in real-world environments. Others delivered higher quality but required months of custom development and deep infrastructure work.
Leonard has explained that developers and enterprises were stuck choosing between speed and control. That gap pushed the founders to rethink how voice agents should be created from the ground up. Their conclusion was clear. Voice agents should be coded, validated, and optimized directly in software, not assembled through brittle visual diagrams.
VoiceRun launched last year with a platform designed to let developers and AI coding assistants build and scale voice agents entirely in code. Instead of clicking through boxes and prompts, teams define behavior programmatically. This approach gives developers fine-grained control and makes it easier to evolve agents over time without breaking complex conversation flows.
According to Leonard, code is the native environment for AI coding agents. He believes these agents perform far better when working directly with structured logic rather than abstract visual tools. That advantage becomes critical as voice products grow more complex and must handle edge cases that visual builders rarely anticipate.
The limitations of diagram-based tools show up quickly in production. Dialect changes, regional phrasing, or nuanced conversational behavior can be difficult to implement if the interface was not designed to support those features. In a code-first system, those adjustments can often be handled with minimal changes, making customization faster and more reliable.
VoiceRun’s platform also includes built-in evaluation tools, A/B testing, and one-click deployment. These features allow teams to iterate rapidly while maintaining quality standards. Developers can test variations, monitor outcomes, and push improvements without rebuilding entire systems from scratch.
The startup is positioning itself squarely toward enterprise developers. Many customers are using the platform to integrate AI into customer service operations or to launch new voice-based products. Leonard has pointed to use cases such as restaurant technology companies deploying AI phone concierges to manage reservations more efficiently.
The $5.5 million seed round was led by Flybridge Capital, giving VoiceRun fresh resources to expand its platform and grow its engineering team. The funding arrives at a time when investment in AI agents remains intense, with billions flowing into the broader sector over the past year.
Competition in the voice agent space continues to accelerate. On one end are no-code tools like Bland and Retell AI, which focus on fast demos and simple deployments. On the other end are highly flexible frameworks such as LiveKit and Pipecat, which give developers maximum control but demand more effort.
Leonard believes VoiceRun occupies the middle ground. The platform offers global voice infrastructure and an evaluation-driven lifecycle while ensuring that customers retain ownership of their business logic and data. That balance aims to deliver speed without sacrificing quality or long-term maintainability.
A key part of VoiceRun’s vision is closing the loop for end-to-end coding agent development. The company expects developers to increasingly supervise AI agents that write code, run tests, deploy updates, and suggest improvements. Voice agents, in this view, become products that continuously evolve rather than static scripts.
Beyond tooling, Leonard wants to shift how people perceive automated voices. Many consumers still feel relief when a human answers the phone because voice automation has often failed to meet expectations. Poor recognition, rigid flows, and awkward responses have shaped years of frustration.
Research supports that skepticism. A survey conducted last year by Five9 found that roughly three-quarters of respondents still prefer speaking with a human for customer service issues. Leonard argues that this preference reflects the limits of today’s tools rather than the potential of voice AI itself.
Human agents also face challenges. Language barriers, fatigue, and unconscious judgment can all affect interactions. Leonard believes well-designed voice agents could remove some of those issues while offering consistent, scalable support across industries.
He often compares the current state of voice AI to the early days of automobiles. Cars existed before mass adoption, but it was the assembly line that made them ubiquitous. In the same way, Leonard sees many impressive voice agents today but believes widespread adoption will only happen once a true voice agent factory exists.
VoiceRun is positioning itself as that factory. With its $5.5 million seed funding, the company aims to give developers the infrastructure, tools, and workflow needed to build voice agents that finally feel reliable, adaptable, and ready for everyday use.
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