Recall.ai has secured $38 million in Series B funding at a valuation of $250 million. The round included Bessemer Venture Partners, HubSpot Ventures, Salesforce Ventures, Y Combinator and several individual backers. The company builds the technical foundation that allows developers to integrate with platforms such as Zoom, Google Meet, Microsoft Teams and Slack Huddles through a single Application Programming Interface (API).
By eliminating the need to create and maintain separate integrations, Recall.ai streamlines one of the most resource-intensive tasks for software teams. The system processes more than three terabytes of video every second and launches over eight million EC2 cloud instances each month.
Metadata from a meeting becomes available within ten seconds of its end, regardless of duration. For developers, this speed translates into shorter build cycles and faster product releases.
Building a Foundation for Conversation Data
The value of conversation data has grown as companies across industries build products that rely on voice and meeting insights. Customer service, healthcare, sales and recruiting all depend on accurate records of spoken exchanges. Developers often face delays when connecting to multiple platforms. Recall.ai’s approach focuses on solving that challenge through one integration point.
David Gu, co-founder and CEO, expressed the motivation: “AI cannot function well without context, and spoken conversation is the largest untapped dataset. We are building the infrastructure that makes it accessible.”
Amanda Zhu, co-founder and COO, added her perspective in a LinkedIn post announcing the raise.
Customers using Recall.ai report measurable efficiency gains. HubSpot’s EVP of Engineering, Jared Williams, described the benefit: “Recall.ai allows us to deliver meeting recording features without worrying about infrastructure or platform-specific issues.”
Companies including HubSpot, ClickUp and Apollo.io have reported that using the platform has accelerated their timelines by a factor of two to three.
The Infrastructure Advantage
Conversation data has become a busy arena, with companies carving out their niches in transcription, analytics, or collaboration. Recall.ai takes a different route, staying behind the scenes as the connective layer developers rely on. Fireflies.ai builds transcription and search tools aimed at employees who want to revisit meeting discussions. Gong.io focuses on revenue intelligence, helping sales teams analyze calls and coach performance. Grain enables workers to capture and share clips from meetings for collaboration.
Recall.ai separates itself by avoiding direct end-user services. It provides the underlying infrastructure through which developers then decide how to shape the user experience, whether in sales software, healthcare tools or customer-support products. In this sense, Recall.ai is closer to a utility provider than a SaaS competitor.
The distinction influences adoption. Companies that want control over their interface and features can rely on Recall.ai to handle the heavy lifting of integration and scaling. Competitors build features on top of data pipelines; Recall.ai focuses on keeping the pipelines reliable and accessible. Each approach answers a different set of market needs.
Expanding Scope Beyond Video
With new capital in hand, Recall.ai is extending its coverage beyond video meetings. The roadmap includes desktop recording, phone dialers, call capture and in-person meeting recording. Developers will be able to access these new channels through the same API they already use for Zoom or Microsoft Teams. The goal is consistency across communication formats, reducing the technical barriers for teams that want to broaden their product capabilities.
The company’s infrastructure has already scaled to millions of meetings every month, and the addition of new form factors indicates a focus on being a single access point for conversation data. For industries where voice and meetings dominate daily work, that level of integration removes complexity from product development and enables faster adoption of conversation-based tools.
For David Gu, the focus remains enabling that access at scale. As he said: “We’re in an AI gold rush, but 99% of the context AI needs is never written down — it’s spoken. To fill out a CRM, AI needs to know what the customer actually said. To write a follow-up email, it needs to know what was discussed. To generate a clinical note, it needs to know exactly what the patient said. Conversation data is the world’s largest untapped dataset, and we’re building the infrastructure that makes it accessible.”
