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Serval Raises $150M to Expand Enterprise AI Automation

Serval Raises $150M at $3B Valuation Serval has raised $150 million in Series C funding to expand its enterprise AI automation platform. Sequoia led the round at a $3 billion The post Serval Raises $150M to Expand Enterprise AI Automation appeared first on Ventureburn .

Serval Raises $150M to Expand Enterprise AI Automation

Serval has secured $150 million in Series C funding, bringing its total capital raised to $277 million. The investment round was led by Sequoia, with participation from Sequoia, Elad Gil, Evantic, First Round, General Catalyst, Redpoint, and Conviction. This funding will be used to accelerate product development and expand Serval's global operations.

Serval's core product, Catalyst, allows employees to automate work using AI agents. Users input tasks in natural language, and the platform generates automations to complete the tasks. Initially targeting IT support, Catalyst has expanded to other enterprise departments. The company reports that its annual recurring revenue has doubled each quarter since its Series B funding.

Serval now aims to service customers in various departments, helping them automate repetitive tasks beyond traditional IT operations. The platform has reportedly helped two Fortune 20 companies achieve organization-wide automation. To address challenges in enterprise AI adoption, Serval is launching ProServal, a professional services division with over 100 Automation Engineers.

These engineers will help organizations identify automatable tasks, create solutions, and design automations tailored to existing business processes. Serval plans to train half of the engineers through a four-week program, enabling rapid learning in the rapidly evolving AI environment. With its strong revenue growth and recent funding, Serval aims to challenge established enterprise software providers like ServiceNow and Workday, which are also investing in AI automation capabilities.

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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