Zaro
Enterprise workspace that unifies context, agents, and custom apps in a single company-owned platform built on your own data.
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Product Overview
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
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