London's Airspeed raises €17.2 million Series A to build AI-powered execution layer for revenue teams

Airspeed, a London-headquartered artificial intelligence platform specialising in revenue operations, has secured €17.2 million in Series A funding to accelerate its expansion into North American markets and strengthen its technological infrastructure. The funding round, announced in early 2026, was orchestrated by venture capital firm DN Capital as lead investor, with additional backing from Vi Partners, Framework Venture Partners, and Atlassian Ventures. The company, founded in 2022 by Adam Liska and Devang Agrawal—both former researchers at DeepMind—positions itself as an autonomous execution layer for go-to-market teams, fundamentally reimagining how revenue organisations interact with their data and processes. This capital infusion represents a significant validation of the emerging category of AI agents designed specifically to automate commercial workflows, distinguishing Airspeed from conventional business intelligence and customer relationship management platforms that have historically served passive informational roles rather than active execution functions.

The emergence of Airspeed within the startup ecosystem reflects a maturing recognition among enterprise technology investors and practitioners that artificial intelligence's transformative potential extends beyond analytics and forecasting into genuine operational automation within revenue functions. For the past three years, the market has witnessed an explosive proliferation of generative AI applications, many of which layer new interfaces atop legacy enterprise software without fundamentally reshaping underlying workflows. Airspeed's founding team deliberately rejected this retrofit approach, instead constructing their platform architecture from foundational principles designed specifically to accommodate autonomous agent deployment. This architectural choice proves consequential at a moment when revenue teams increasingly face dual pressures: accelerating sales cycles driven by competitive intensity and customer expectations, whilst simultaneously managing exponential increases in data complexity across multiple communication channels. The funding round's composition—particularly the involvement of Atlassian Ventures, signalling enterprise software incumbency recognition of generative AI's operational implications—underscores institutional awareness that the next generation of revenue tools must transcend dashboard interfaces to deliver genuine automation.

Airspeed's operational metrics, disclosed within the funding announcement, provide concrete evidence of market traction that extends beyond investor appetite. The platform claims to have doubled its workforce over the preceding twelve months whilst simultaneously quadrupling revenue generation, suggesting accelerating commercial momentum rather than growth plateau typical of early-stage platforms. More significantly, the company operates across a customer base spanning 200 organisations distributed across twenty countries, indicating geographic diversification unusual for London-based software companies still in early growth phases. The platform's customer engagement patterns reveal particularly striking usage intensification: during the initial four months of 2026, users created thousands of custom agents on the platform, with monthly execution volumes nearly tripling between January and April alone. A case study involving Foleon, an enterprise content governance platform, documented concrete quantitative outcomes including $193,000 in cost recovery and six additional productive hours recovered per sales representative weekly within ninety days of implementation—metrics that translate directly into measurable productivity gains rather than theoretical efficiency improvements.

For revenue operations professionals and sales technology decision-makers, Airspeed's value proposition addresses a critical operational gap that existing enterprise platforms deliberately left unfilled. Contemporary sales organisations typically maintain sophisticated customer relationship management systems functioning as passive repositories of transactional data, and business intelligence tools capable of sophisticated analysis but requiring human interpretation and manual follow-up. Airspeed's autonomous agents operate between these systems, automatically executing actions derived from commercial context: updating customer relationship management records based on email and call activity, flagging contract risks requiring human attention, generating follow-up communications matched to specific deal stages, and coordinating actions across fragmented communication channels including email, video calls, ticketing systems, and existing CRM platforms. This execution capacity directly addresses a pervasive source of friction in modern sales operations: the manual labour of translating analytical insights into actionable tasks, a process that typically consumes disproportionate amounts of revenue team attention whilst contributing minimal strategic value. For organisations managing complex enterprise sales processes across dispersed teams, this automation layer potentially unlocks material time recovery and decision-quality improvements by ensuring systematic action on commercially relevant signals that human teams would otherwise miss or deprioritise during periods of high transaction volume.

This funding development signals broader industry convergence around the architectural principle that enterprise artificial intelligence's highest-impact applications emerge when AI systems operate autonomously within established human workflows rather than attempting wholesale replacement of institutional processes. The investor participation pattern—combining specialist venture firms experienced in enterprise software infrastructure alongside strategic corporate investors from established technology vendors—reflects emerging consensus that agent-native platforms represent a structural shift in how organisations will operationalise artificial intelligence across functional domains. Airspeed's particular positioning within revenue operations proves instructive because sales processes combine high human capital intensity, substantial economic stakes per transaction, and complex multi-party interactions that create particular demand for intelligent automation. The proliferation of autonomous agents across Airspeed's customer base, particularly the user-generated custom agents deployed across Foleon and other customers, suggests adoption patterns moving beyond top-down vendor dictation toward organic extension of platforms by organisational users themselves. This pattern indicates maturation away from the initial wave of large language models toward purpose-built agent systems calibrated to specific functional domains and backed by institutional knowledge of how particular business processes execute.

Revenue operations teams should monitor Airspeed's product evolution and market expansion trajectory closely throughout 2026 and beyond, particularly regarding whether the company successfully translates its European market presence into durable North American market share following this funding announcement. The specific measurement of platform utilisation—monthly execution volume trending and custom agent creation rates—represents a transparent proxy for whether organisations are adopting Airspeed as peripheral productivity enhancement or integrated operational infrastructure. Additionally, observers should track competitive responses from established enterprise software vendors including Salesforce, HubSpot, and Microsoft, which possess substantial resources to develop competing agent infrastructure for their existing customer bases. The technical architecture Agrawal and Liska have constructed around unified commercial context understanding and guardrailed agent runtime execution represents the substantive competitive moat; vendors capable of achieving similar contextual richness whilst maintaining appropriate human oversight and auditability across autonomous actions will determine whether Airspeed maintains category leadership. By late 2026, revenue organisations should expect material clarification regarding whether agent-native platforms become standard infrastructure or remain specialised solutions for particular operational niches, a determination that will significantly influence technology spending priorities across enterprise software categories.