Finance and revenue teams have spent years navigating a fragmented analytics landscape built around dashboards, spreadsheets, disconnected systems, and manual reporting. While AI has steadily entered the enterprise stack, many organizations still struggle to turn data into timely, actionable decisions without relying heavily on analysts and technical teams.
That is the problem Arito AI says it is trying to solve. The company, founded by Daniel Zahavi and Michael Estrin, announced a $6 million seed round led by Amplify Partners, with participation from two angel investors who are both seasoned CFOs. With offices in Tel Aviv and Palo Alto, Arito AI plans to use the funding to expand its engineering and go-to-market teams while continuing to develop its agentic analytics and monitoring platform for finance and revenue organizations.
Moving Beyond Static Dashboards
Arito AI is positioning itself around what it describes as “agentic analytics,” a model that shifts analytics from static reporting toward continuously operating AI-driven systems capable of monitoring, analyzing, and acting on business data in real time.
According to the company, its platform is designed to eliminate many of the technical hurdles traditionally associated with analytics deployments. Rather than relying on manual data modeling or complex integrations, the platform uses autonomous data onboarding to interpret the structures of commonly used finance and revenue systems.
“At Arito, we believe every business team should be able to operate with real-time intelligence, securely, and without waiting on analysts or outdated dashboards,” said Daniel Zahavi, CEO of Arito AI. “This funding allows us to double down on our vision of making insights truly self-serve, proactive, and actionable through intelligent agents that understand the business context and adhere to rules and permissions defined by the organization while maintaining full data lineage.”
The platform allows users to interact with data through natural language, enabling them to create self-updating dashboards, analyze scenarios, and configure notifications without requiring technical expertise. Arito AI says its core capabilities include text-to-dashboard creation, multi-user collaboration with AI agents, and AI-driven real-time updates tied to key metrics and events.
Governance And Security At The Center
As AI systems become more deeply embedded into enterprise workflows, questions around governance and data access are becoming increasingly important, particularly for finance teams handling sensitive information.
Arito AI says its platform was built with a zero-data-exposure architecture intended to address those concerns. Central to the approach is a unified Role-Based Access Control framework that governs who can access specific data across systems, tools, and spreadsheets.
The company says the RBAC layer extends even to applications and datasets that historically lacked granular permissions, including spreadsheets at the cell level. That capability is intended to provide tighter control over how employees and AI agents interact with information inside organizations.
Mike Dauber, GP at Amplify Partners, said the company’s approach addresses a longstanding challenge in enterprise analytics.
“Arito is tackling one of the most persistent challenges in modern organizations: the gap between data availability and data usability,” said Dauber. “Their agentic approach removes the friction from analytics and empowers finance and revenue teams to act faster and with greater confidence.”
Dauber also pointed to the broader implications of AI-driven analytics systems operating autonomously inside enterprises.
“As companies move toward agentic analytics and continuous monitoring, where AI systems proactively analyze and act on business data, the stakes for security rise dramatically,” Dauber continued. “Arito’s architecture stands out not only by creating a unified control plane for user permissions, but by extending RBAC to systems that never supported it before. That combination is critical for enabling safe, enterprise-wide adoption of AI.”
Building Collaborative AI Workflows
Another area Arito AI is emphasizing is collaboration between employees and AI systems. The company says users can work alongside intelligent agents in shared environments, using natural language commands to build dashboards and create AI-driven alerts around important business events.
The platform also includes patent-pending technology that allows users to teach AI agents how specific analyses should be performed by providing real-world examples. According to the company, that capability is designed to help organizations create more consistent and repeatable analytical workflows without ongoing manual intervention.
Thomas Seifert, CFO at Cloudflare, said he sees agentic analytics evolving beyond traditional self-service business intelligence models.
“The future of analytics is not just self-service; it’s autonomous and collaborative,” said Seifert. “Arito is redefining how organizations interact with their data, turning it into a continuous, intelligent feedback loop.”
As enterprise AI adoption accelerates, companies across the analytics market are increasingly competing around automation, governance, and real-time intelligence. Arito AI is betting that finance and revenue teams will demand systems capable not only of surfacing information, but also of continuously interpreting and operationalizing it within the guardrails organizations require.
