Zaro

What is Zaro?

Zaro is a London-based enterprise platform founded in 2026 by Michael Bajwa and Qian Zheng, veterans of Convergence and Salesforce's Agentforce team. It solves a core fragmentation problem in enterprise AI: organisational knowledge — decisions, workflows, documents, operational history — is scattered across dozens of disconnected tools, owned by vendors rather than the company itself. Zaro inverts this by providing a persistent shared context layer where agents, data, and custom applications all operate as a closed loop. The more the platform is used, the smarter and more contextually aware it becomes. Zaro is model-agnostic, routing routine tasks to lower-cost models and reserving frontier models for complex work, reducing costs by up to 10×. It raised $5.1M in pre-seed funding led by Cherry Ventures, with backing from Hugging Face's Thomas Wolf, GitHub's Thomas Dohmke, and Convergence co-founders.

Key Features

• Shared Context Layer

  A persistent, company-owned intelligence layer that stores decisions, workflows, documents, and operational history so every agent and app draws from the same accumulated knowledge.

• Custom Agent Builder

  Teams can build and deploy context-aware AI agents tailored to their own data and internal processes, without relying on third-party vendor infrastructure.

• No-Code App Creation

  Custom internal applications can be built in plain language from company documents, meeting notes, and data — no engineering required.

• Model-Agnostic Routing

  Automatically routes tasks by complexity, assigning routine work to cost-efficient models and reserving frontier models for high-demand tasks — cutting costs by up to 10×.

• Workflow Marketplace

  Pre-configured workflows are available alongside custom builds, enabling teams to deploy common automations quickly and connect to tools like Slack and Notion.

• Company-Owned Data Portability

  All context, outputs, and agent memory remain fully owned and portable by the organisation — not locked into a software vendor's ecosystem.

Use Cases

• Enterprise Knowledge Management : Organisations consolidate scattered institutional knowledge — meeting notes, decisions, internal docs — into one shared context that all agents and apps can access.
• Workflow Automation : Operations and business teams automate repetitive, multi-step processes by deploying agents that retain context across tasks rather than starting from scratch each time.
• Internal Tool Building : Non-technical teams build custom internal applications directly from their own data in natural language, reducing dependency on engineering resources.
• Cross-Tool AI Coordination : Enterprises replace fragmented point solutions with a single workspace where agents, integrations, and apps share the same context and compound over time.
• Cost-Optimised AI Deployment : Finance and IT teams reduce AI spend by routing tasks intelligently across model tiers rather than defaulting every request to expensive frontier models.

FAQs