Defining an AI-Native Workspace and Zaro’s $5.1M Bet
An AI-native workspace is an integrated environment where enterprise data, workflows, and AI agents operate on a shared context layer so that knowledge created in one application can inform actions and decisions across many other tools and processes. Zaro has raised $5.1 million (approx. RM23.5 million) in pre-seed funding to build this kind of enterprise AI workspace, aiming to connect scattered tools, automations, and datasets in a single adaptive platform. Backed by Cherry Ventures and several notable angel investors, the startup is emerging from stealth with a clear mission: reduce fragmentation in how enterprises adopt AI. Instead of adding one more isolated bot or assistant, Zaro wants to sit underneath many AI applications, allowing them to share organizational memory. This move reflects growing demand for unified AI platforms that make existing investments in models and tools work together rather than in silos.
From Fragmented Tools to a Shared AI Context Layer
Many companies now run AI agents, automation platforms, and workflow tools side by side, with each system generating its own local knowledge that rarely travels beyond its boundaries. As a result, decisions, documents, and operational history often remain trapped inside point solutions, making it hard to reuse insights and build cumulative intelligence. Zaro targets this gap with an AI context layer that connects company data, decisions, workflows, and past operations into a single, queryable backbone. On top of this backbone, AI agents and applications can read and write context, so outcomes in one process improve performance in another. Co-founder Michael Bajwa summed up the problem by saying they “built agents that worked flawlessly in isolation and watched them struggle to work together,” highlighting why context, not just smarter models, has become a priority for enterprise AI workspace design.
An Enterprise AI Workspace Built Around Integration, Not Point Tools
Zaro’s approach contrasts sharply with wave after wave of narrow AI tools that tackle one workflow at a time. Instead of focusing on a specific function such as customer support or document search, the company is building a unified AI platform that emphasizes enterprise data integration and workflow connectivity at the foundation. The shared AI context layer is paired with application-building tools and a marketplace of pre-configured workflows, so teams can assemble custom applications from their own documents, meeting notes, and business systems. This design means enterprises can bring their existing processes into a single AI-native workspace rather than replacing everything tool by tool. By treating context as the primary asset and applications as interchangeable surfaces, Zaro aims to make the organization’s accumulated knowledge the stable center, while models and interfaces can change as the AI landscape evolves.
Multi-Model Orchestration and Reducing AI Tool Sprawl
On top of its context layer, Zaro uses a multi-model approach that routes routine tasks to lower-cost models and reserves more advanced models for complex workloads. According to co-founder and CTO Qian Zheng, “Models become increasingly interchangeable over time, but the value created from an organisation’s accumulated knowledge remains unique,” underscoring why the company prioritizes data and context over any single frontier model. This strategy supports a future where enterprises may rely on many different AI vendors without multiplying complexity. Instead of managing separate bots, each with its own memory and configuration, organizations can standardize on one enterprise AI workspace while swapping models underneath. Early internal use across functions like HR, finance, and facilities suggests Zaro is positioning its platform as an operational spine that can absorb tool sprawl rather than add to it.
