Airspeed, the London- and New York-based startup building an AI-powered execution layer for revenue teams, has raised $20 million in Series A funding. The round was led by DN Capital, with participation from Vi Partners, Framework Venture Partners, and Atlassian Ventures, bringing the company’s total funding to more than $25 million.
The funding comes just weeks after the company rebranded from Glyphic to Airspeed, reflecting a broader shift in how it positions its platform. While the original name focused on the underlying technology, Airspeed emphasizes the outcome: helping revenue teams move faster by turning commercial insights into automated action.
Founded in 2022 by former Google DeepMind researchers Adam Liska and Devang Agrawal, Airspeed is building what it calls a “system of action” for modern go-to-market teams. Rather than simply surfacing insights from customer conversations, emails, support tickets, and CRM systems, the platform deploys autonomous AI agents that execute workflows automatically—updating records, flagging risks, generating follow-ups, and helping sales teams keep deals moving without manual intervention.
"Revenue teams have systems of record and systems of intelligence. What they don't have is a system of action—one that understands their unique commercial context and does the work. Dashboards weren't wrong; they were incomplete. The next leap comes from agents that close the loop."
Liska brings experience from DeepMind, Spotify, Meta, and IBM, where he worked on machine learning and natural language processing. His co-founder, CTO Devang Agrawal, previously worked at DeepMind and Apple and holds a Master of Engineering degree from the University of Cambridge. Agrawal was awarded the prestigious Dr. Manmohan Singh Scholarship, given annually to a select group of outstanding students of Indian origin at Cambridge.
Since its launch, Airspeed has rapidly expanded its engineering and product teams with talent from companies including Meta, Apple, and Spotify, while doubling headcount over the past year. The startup is already showing significant commercial traction. Airspeed serves more than 200 customers across 20 countries, including Persona, Pricefx, Light, and Qdrant. During the first four months of 2026 alone, customers built thousands of custom AI agents on the platform, while monthly agent activity nearly tripled between January and April. The company also reports 4x revenue growth over the past 12 months.
One customer, enterprise content platform Foleon, reportedly saved more than $193,000 and reclaimed six hours per sales representative each week within the first 90 days of deploying Airspeed.
"Most teams retrofit AI onto legacy systems. We built the foundation from scratch. Airspeed combines a unified understanding of commercial context with an agent runtime and rigorous evaluation framework, enabling agents to act on live deals rather than stale snapshots of data."
The fresh capital will be used to accelerate product development and expand operations as Airspeed competes in one of the fastest-growing segments of enterprise software. The company operates in a crowded market that includes AI-native sales startups such as 11x, as well as established revenue software leaders like Gong, Salesloft, Outreach, and Salesforce. Airspeed’s bet is that the next generation of enterprise AI won’t simply provide insights—it will execute tasks autonomously across disconnected systems.
"Every CEO wants a single source of truth for what is driving the business. The next wave of enterprise AI belongs to systems that turn insight into execution. Airspeed is building that system of action for modern organisations."
Investors see the company as part of a broader shift toward agent-native enterprise software. “The next wave of enterprise AI belongs to agents that act on deep context, not chat that summarizes it,” added Georgia Zhang, Head of Atlassian Ventures.
With global spending on AI-powered sales technologies projected to grow dramatically over the coming decade, Airspeed is positioning itself at the intersection of automation, enterprise workflows, and AI agents. The challenge now is scale: moving fast enough to establish itself as the category leader before larger incumbents decide the execution layer is the next strategic battleground.
