London-based startup Zaro has emerged from stealth with $5.1 million in pre-seed funding to develop a platform designed to unify enterprise AI tools, workflows, and data within a single adaptive workspace. The round was led by Cherry Ventures, with participation from angel investors including Thomas Wolf, co-founder of Hugging Face, GitHub CEO Thomas Dohmke, Mandeep Singh, Charlie Songhurst, and former Convergence founders Marvin Purtorab and Andy Toulis.
Founded by Michael Bajwa and Qian Zheng, Zaro is developing a platform designed to address the fragmentation of AI tools, applications, and data across organisations.
Several members of Zaro's eight-person team previously worked at AI startup Convergence, where they developed AI agents before the technology entered the mainstream. Following Salesforce's acquisition of Convergence, members of the team contributed to the development of Agentforce, Salesforce's AI platform.
Businesses often deploy AI agents, automation platforms, and workflow tools independently, creating disconnected systems where knowledge generated in one application is not retained or shared across the wider organisation. As a result, institutional knowledge remains fragmented and difficult to reuse.
Zaro's platform is designed to provide a shared context layer that connects company data, decisions, workflows, and operational history. AI agents, applications, and workflows operate on top of this layer, allowing information generated through one process to inform future tasks and interactions across the organisation.
We built agents that worked flawlessly in isolation and watched them struggle to work together. The intelligence never compounds because the context never carries over. Zaro is designed to address that challenge,
said CEO and co-founder Michael Bajwa.
Zaro combines a shared context layer with application-building tools and a marketplace of pre-configured workflows. The platform allows companies to create custom applications based on their own documents, meeting notes, operational processes, and business data.
The company also uses a multi-model approach that routes routine tasks to lower-cost AI models while reserving more advanced models for complex workloads. According to Zaro, this can significantly reduce operating costs compared with deployments that rely exclusively on frontier models.
Context compounds. Models become increasingly interchangeable over time, but the value created from an organisation's accumulated knowledge remains unique,
said co-founder and CTO Qian Zheng.
The platform is already being used internally by Zaro to manage functions including human resources, finance, and facilities operations.
The new funding will support product development, team
growth, and the expansion of the platform as Zaro prepares to bring its
AI-native workspace to a broader group of enterprise customers.
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