Airspeed Raises €17.2M Series A for AI Revenue Execution

Airspeed Secures €17.2 Million Series A to Redefine How Revenue Teams Execute with AI

There is a quiet but powerful shift happening inside the world's most competitive sales organisations. For years, businesses have poured enormous resources into building better dashboards, smarter CRMs, and more sophisticated analytics platforms — all in the hope that better visibility would translate into better performance. But somewhere between the insight and the action, the gap remained stubbornly wide. Sales reps still spent hours updating records, writing follow-up emails, and chasing down context that should have been at their fingertips. Revenue leaders still watched deals stall not because their teams lacked information, but because turning that information into timely, consistent action was simply too slow and too manual a process. That is the problem that London-based startup Airspeed was built to solve — and the market appears to be paying close attention.

Airspeed, an agent-native platform designed specifically for go-to-market (GTM) execution, has just announced the close of a €17.2 million Series A funding round, equivalent to roughly $20 million. The round was led by DN Capital, a prominent European venture capital firm with a long track record of backing ambitious technology companies, with additional participation from Vi Partners, Framework Venture Partners, and Atlassian Ventures. The fresh capital will be used to scale Airspeed's proprietary technology platform, expand its growing team with new global talent, and deepen its footprint in the United States, where demand for intelligent sales execution tools has been accelerating rapidly.

This funding milestone brings Airspeed's total capital raised to more than €21.5 million, or approximately $25 million, since the company was founded just four years ago. What started as a small bet on a big idea has now attracted some of the most respected names in venture, including Point72 Ventures and Creator Fund, alongside the investors who led this latest round. For a company that only recently emerged from its early-stage phase, this level of investor conviction is notable — and it speaks directly to the strength of what Airspeed has quietly been building.

From DeepMind Labs to the Revenue Frontier: The Origin Story of Airspeed

To understand why Airspeed's approach to AI feels fundamentally different from what most enterprise software companies have been building, it helps to look at where its founders came from. Adam Liska, CEO and co-founder, and Devang Agrawal, CTO and co-founder, are both former research scientists from DeepMind, one of the world's most respected artificial intelligence research institutions. Before starting Airspeed, they spent years working on some of the most complex problems in machine learning — problems that required not just building smart models, but building systems that could reliably act in the real world without breaking down.

That research background is not incidental to what Airspeed is today. It is foundational. When Liska and Agrawal turned their attention to the world of sales and revenue operations in 2022, they did not approach it the way most enterprise software founders do — by adding a thin layer of AI on top of existing workflows. Instead, they asked a harder, more fundamental question: what would a revenue platform look like if it were designed from the ground up for a world where AI agents, not humans, handle the bulk of repetitive commercial tasks?

The company was originally incorporated under the name Glyphic, and it raised over €5 million in pre-seed funding in 2023 to explore that initial thesis. The early product was framed as an AI copilot for sales teams, helping representatives process call transcripts and customer conversations more efficiently. But as the team iterated and listened more closely to what their enterprise customers actually needed, the vision evolved into something significantly more ambitious. The rebrand to Airspeed was not just cosmetic — it signalled a fundamental shift in what the company believed it was building. This was no longer a tool to help salespeople work faster. It was an execution layer meant to handle entire categories of revenue work autonomously, at scale, and with enterprise-grade reliability.

The Architecture of Commercial Intelligence: What Airspeed Actually Does

At the heart of Airspeed's platform is a deceptively simple insight: that most AI tools deployed in enterprise sales environments fail not because the underlying models are weak, but because those models lack genuine contextual understanding of the business they are supposed to be helping. A generic large language model can write a follow-up email, but it does not know which deal is at risk, why a particular prospect went cold three weeks ago, or what objections came up during a call last Thursday. Without that layer of commercial context, AI agents make generic, often unhelpful decisions — and enterprise buyers notice.

Airspeed's solution is to build what it describes as the "commercial brain" for modern organisations. The platform's architecture is structured across three interconnected layers, all of which are built on a unified understanding of each company's specific commercial context. The first layer is a persistent memory system that continuously ingests and organises information from across the revenue workflow — calls, emails, CRM entries, support tickets, and any other touchpoint where customer conversations leave a trace. This ensures that Airspeed's agents always operate on a live, accurate picture of each deal, not a stale snapshot pulled from a database that was last updated days ago.

The second layer is the agent runtime itself, which is where autonomous AI agents are deployed to execute specific revenue tasks. These agents do not operate in isolation or in a generic way — they are purpose-built for the commercial context of each individual organisation, which means they understand the specific language, processes, and priorities that matter to that business. They handle everything from automatically updating CRM records after a customer call, to flagging early warning signs that a deal is drifting off track, to generating personalised follow-up communications that reflect the actual substance of recent conversations. The third layer is a rigorous evaluation framework that Airspeed has built to ensure every action its agents take is trustworthy and auditable, which is a critical consideration for enterprise customers who cannot afford to have autonomous systems making consequential mistakes with their most important client relationships.

What makes this architecture genuinely powerful, according to the company, is that it does not require businesses to rip out their existing technology stack. Airspeed is built to sit on top of the tools that revenue teams already rely on, integrating with the CRMs, communication platforms, and data systems that are already part of their daily workflows. Rather than forcing companies to choose between their current setup and the benefits of AI automation, Airspeed enhances what is already there — and does so by doing the work that previously fell through the cracks.

Devang Agrawal, CTO and co-founder, explained the thinking behind the platform's design clearly: most companies that try to add AI to their sales processes end up retrofitting it onto legacy systems that were never designed with agents in mind. Airspeed, by contrast, was built from scratch with the assumption that AI agents would be doing real work — which means the foundation, the runtime, and the evaluation framework all had to be engineered specifically to support that goal. Every action the platform takes is meant to reflect what is actually happening in a live deal, not a record that may already be out of date by the time an agent reads it.

Investor Conviction and the Strategic Rationale Behind the Funding Round

The composition of Airspeed's investor syndicate tells its own story about where enterprise AI is heading. DN Capital, which led this Series A, has consistently backed companies that are not just riding technology trends but actively reshaping how businesses operate at a fundamental level. The firm's decision to lead this round reflects a broader thesis that the next wave of enterprise AI will be defined not by platforms that give leaders better information, but by platforms that turn information into action, consistently and at scale.

Thomas Rubens, Partner at DN Capital, framed the investment thesis directly: every chief executive wants a single source of truth for what is driving their business. The first generation of enterprise data tools delivered on visibility — dashboards, analytics, business intelligence platforms gave leaders an unprecedented window into their operations. But visibility alone does not close deals, recover at-risk accounts, or keep customers engaged. The competitive advantage now belongs to companies that can act on what they see, faster and more consistently than their rivals. That is precisely the gap that Airspeed is designed to close.

The participation of Atlassian Ventures is also worth noting. Atlassian, the company behind widely used enterprise collaboration tools like Jira and Confluence, has developed an unusually sharp understanding of how teams actually work — where communication breaks down, where context gets lost between systems, and where automation can genuinely reduce friction rather than just adding complexity. The fact that Atlassian's venture arm chose to back Airspeed suggests a degree of conviction not just in the product, but in the team's understanding of how enterprise workflows operate in practice.

Vi Partners and Framework Venture Partners round out a syndicate that brings both European institutional capital and cross-Atlantic network depth to Airspeed as it scales. For a company that is growing its presence in both London and New York simultaneously, having investors who understand both markets is a meaningful strategic asset.

Numbers That Validate the Vision: Airspeed's Growth Trajectory

Funding rounds are announcements, but growth metrics are evidence — and Airspeed's numbers paint a compelling picture of a product that is resonating with enterprise buyers in a real and durable way. Over the past twelve months, the company has doubled its headcount and, more tellingly, quadrupled its revenue. In a market where many AI startups are struggling to convert early enthusiasm into sustained commercial traction, those growth rates are meaningful signals that Airspeed has found genuine product-market fit.

The platform currently serves 200 customers spread across 20 countries, a geographic breadth that speaks to the universality of the problem Airspeed is solving. Revenue operations challenges are not unique to any one market or industry — any company with a sales team that relies on CRM data, customer conversations, and multi-touch sales cycles faces the same fundamental execution gap. Airspeed's customer list includes names like Persona, Pricefx, Light, and Qdrant, spanning sectors from identity verification to pricing software to vector database technology, which underscores just how cross-sectoral the demand for this kind of AI execution platform has become.

Perhaps the most striking figure in the company's recent disclosures concerns agent adoption. During the first four months of 2026 alone, Airspeed's customers created thousands of custom agents on the platform. Monthly run volumes nearly tripled between January and April — a growth rate that suggests customers are not just testing the technology but actively building it into their core revenue workflows. When enterprise organisations begin deploying custom agents at that velocity, it is a strong signal that the platform has moved beyond pilot phase and into genuine operational dependency.

The case study that perhaps best illustrates what Airspeed's platform can deliver in practice comes from Foleon, an enterprise content creation and governance platform. Within the first 90 days of deploying Airspeed, Foleon reported saving more than $193,000 and recovering six hours per sales representative each week. Six hours per rep, per week — that is a staggering amount of time recaptured, time that can be redirected toward the genuinely human elements of the sales process: building relationships, navigating complex negotiations, and solving problems that no autonomous agent can address on its own. Multiplied across an entire sales team and sustained over a full year, the compounding effect of that efficiency gain is enormous.

What Lies Ahead: Airspeed's Road Map and the Broader Shift in Enterprise AI

The fresh capital from this Series A is not being deployed in a vacuum — it reflects a specific, well-considered vision for where Airspeed wants to be in the next two to three years. Scaling the proprietary technology platform is the most immediately pressing priority, given how rapidly customer demand is growing and how technically intensive it is to maintain the kind of real-time commercial context that makes Airspeed's agents genuinely useful rather than generically capable. As the volume of agents running on the platform continues to increase, the underlying infrastructure needs to scale with it, without compromising on the reliability and trustworthiness standards that enterprise customers require.

Hiring is the second major area of investment. Airspeed is actively recruiting global talent, with a particular focus on roles that bridge deep AI research and practical revenue operations expertise — a combination that mirrors the profile of its founders and reflects the company's belief that the best enterprise AI products are built by people who genuinely understand both the technology and the human workflows it is meant to augment. The expansion of its US presence is closely tied to this hiring push, as North America represents the largest addressable market for the kind of enterprise GTM tooling that Airspeed is building.

At The AI World, we see Airspeed's funding milestone as part of a broader and accelerating shift in how the enterprise technology sector thinks about artificial intelligence. The first wave of enterprise AI was largely focused on analysis — making it easier for businesses to understand their data, their customers, and their performance. The second wave, which Airspeed exemplifies, is focused on execution. The question is no longer just what is happening in my pipeline, but what should my organisation do about it, right now, today, and who or what is going to actually do it. That shift from insight to action is arguably the most consequential transition in enterprise technology since the original SaaS revolution, and the companies that build the most reliable, most contextually intelligent execution layers will occupy an extraordinarily valuable position in the business software landscape for years to come.

Airspeed is staking a serious claim to that position. With a founding team trained at one of the world's leading AI research labs, a platform architecture that was designed from first principles rather than retrofitted from legacy assumptions, a customer base spanning 200 organisations in 20 countries, and now a well-capitalised balance sheet to fuel its next stage of growth, the London-based startup is well-positioned to play a defining role in the future of how revenue teams operate. The combination of technical depth, commercial traction, and investor backing it has assembled is the kind of foundation that serious enterprise AI companies are built on — and this Series A is very much just the beginning.