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Kindo Raises $30 Million to Expand Enterprise AI Control Platform

Kindo Raises $30 Million to Strengthen Enterprise AI Management Kindo, an enterprise AI management firm, raised a $30 million round. The financing was led by Crosspoint Capital and Forgepoint Capital. The post Kindo Raises $30 Million to Expand Enterprise AI Control Platform appeared first on Ventureburn .

Kindo Raises $30 Million to Expand Enterprise AI Control Platform

Enterprise AI management company Kindo has raised $30 million in a new financing round. Crosspoint Capital and Forgepoint Capital led the investment, with TFX Capital, Circle Rock Capital, Drive Capital, Riot Ventures, RRE Ventures, Eniac Ventures, Marlinspike Partners, New Era Ventures, and Sunset Venture also participating. The additional capital brings Kindo's total funding to $57.6 million.

The funds will be used to expand Kindo's go-to-market organization, develop its enterprise AI Control Center, and enhance its Deep Hat models and add-ons.

Kindo's AI Control Center centralizes the discovery, management, and monitoring of enterprise AI agents. It detects shadow AI in organization environments and aggregates agent activity, permissions, costs, and results into a single view. This addresses the growing issue of enterprises using autonomous agents for various tasks without IT, Security, Finance, and Compliance teams being aware of their existence.

Kindo's gateways track and audit agent calls to linked data sources and oversee engagements with software and sanctioned AI models.

To address the rising costs of running autonomous AI systems, Kindo offers Deep Hat products. These tools help enterprises track AI agent expenses, identify inefficient operations, and manage costs through detailed spending analytics. Finance teams can review each agent's activities, compare outputs to inputs, set budgets, and receive alerts for budget caps. Unproductive agents and excessive model usage can be identified with Kindo's Deep Hat products.

Written by urgent.news from Ventureburn's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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