Airspeed Raises $20M Series A to Build an AI ‘Commercial Brain’ for Revenue Teams

Airspeed, the London- and New York-based AI startup founded by former DeepMind researchers, has raised a $20 million Series A round led by DN Capital, with participation from Vi Partners, Framework Venture Partners, and Atlassian Ventures. The funding brings the company’s total capital raised to more than $25 million as it looks to expand its AI-powered platform for sales and revenue operations teams.

The company, formerly known as Glyphic, recently rebranded to Airspeed as it broadened its vision beyond conversation intelligence into what it describes as an execution layer for revenue organizations. Rather than simply surfacing insights from meetings, emails, and CRM systems, Airspeed’s platform is designed to take action on those insights through autonomous AI agents that operate across commercial workflows.

Moving Beyond Revenue Intelligence

Over the past decade, sales technology has largely focused on helping organizations collect more data and generate more analytics. Revenue intelligence platforms can identify risks, highlight opportunities, and provide visibility into customer interactions, but much of the follow-up work still falls on human teams.

Airspeed is positioning itself as the next evolution of that software stack. The platform deploys AI agents that monitor customer conversations, emails, support tickets, and CRM data, then automatically execute tasks such as updating records, generating follow-up actions, identifying deal risks, and coordinating workflows across teams.

The company’s founders argue that organizations already have systems of record and systems of intelligence. What is missing, they contend, is a “system of action” capable of turning insights into execution without requiring employees to manually connect the dots.

Built by Former DeepMind Researchers

Airspeed was founded in 2022 by former DeepMind research scientists Adam Liska and Devang Agrawal. Since its launch, the company has assembled a team with experience from organizations including Meta, Apple, and Spotify.

That research background appears to be reflected in the platform’s architecture. According to the company, Airspeed’s technology is built around a unified understanding of an organization’s commercial context. Rather than relying on isolated data sources, the platform creates a persistent memory layer that centralizes knowledge across the entire go-to-market process.

This “commercial brain” serves as the foundation for a growing library of AI agents that can execute specialized tasks across sales, customer success, and revenue operations workflows. The company’s emphasis on context is notable, as many enterprise AI deployments continue to struggle with fragmented information spread across multiple business systems.

Strong Growth Signals

The funding announcement arrives amid significant growth for the company.

Airspeed reports serving 200 customers across 20 countries, including organizations such as Persona, Pricefx, Light, and Qdrant. Customers built thousands of custom AI agents on the platform during the first four months of 2026 alone, while monthly execution volume nearly tripled between January and April.

The company also says it has achieved fourfold revenue growth over the past year while doubling its headcount. One customer, Foleon, reportedly saved more than $193,000 and recovered approximately six hours per sales representative per week during its first 90 days of deployment.

Those figures suggest growing demand for AI systems that can automate operational work rather than simply provide recommendations.

The Emerging Market for AI Execution Platforms

Airspeed’s rise reflects a broader shift occurring across enterprise AI.

The first wave of generative AI focused primarily on content creation and knowledge retrieval. The next phase appears increasingly centered on AI agents capable of taking actions on behalf of users. Rather than generating a report about a sales opportunity, an AI system can now update CRM records, schedule follow-ups, notify stakeholders, and execute predefined workflows automatically.

This evolution requires more than advanced language models. It depends on systems that maintain organizational context, understand business processes, and operate within carefully designed guardrails. Airspeed’s platform is built around this concept, emphasizing trustworthy execution rather than standalone AI conversations.

The Broader Implications of AI Execution Systems

The emergence of platforms like Airspeed highlights a shift in how enterprises are approaching artificial intelligence. The first generation of business AI largely focused on helping employees work faster by generating content, summarizing information, or answering questions. Increasingly, however, organizations are looking for systems that can move beyond recommendations and take action within existing workflows.

This evolution raises important questions about the future role of human workers in sales, customer success, and operations teams. Rather than replacing employees outright, execution-focused AI may reduce the amount of time spent on administrative tasks such as CRM updates, pipeline management, meeting follow-ups, and internal coordination. The result could be smaller teams managing larger customer bases while focusing more on relationship building and strategic decision-making.

At the same time, the technology introduces new challenges around oversight, accountability, and trust. As AI agents gain the ability to update systems, trigger workflows, and influence commercial decisions, organizations will need stronger governance frameworks to ensure actions remain accurate, auditable, and aligned with business objectives.

The next few years will likely determine whether AI agents become a standard layer of enterprise software or remain limited to specialized use cases. If adoption continues to accelerate, the distinction between software that provides information and software that executes work may gradually disappear, fundamentally changing how revenue organizations operate.