Labelbox Secures $100 Million in Series E Funding

San Francisco-based Labelbox raises new capital to advance its AI training infrastructure and managed agent technology.

Updated on Oct. 22, 2026 in Artificial Intelligence

Bold vector editorial illustration of modular metallic block forms representing technical infrastructure, with clean outlines and a vibrant teal and coral palette. — Labelbox secured over $100 million in Series E funding led by Infinitum Partners to scale its AI evaluation and infrastructure products. AI Illustration. Upload story photo >

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Labelbox has secured more than $100 million in Series E financing to bolster its artificial intelligence development platform. The San Francisco-based company reached profitability in 2026 and currently serves over 90% of major U.S. AI labs.

Why it matters

The fresh capital supports continued research and engineering for products that enable AI models to learn from real-world experience. By expanding its teams, the company aims to scale its infrastructure for existing and future enterprise customers.

Labelbox provides reinforcement learning environments through its Horizon product and enterprise-grade managed agents via Recursion Managed Agents. The platform currently supports more than 400 customers across the industry.

The players

Labelbox

A San Francisco-based technology company that provides infrastructure for AI evaluation and the deployment of autonomous agents.

Infinitum Partners

The investment firm that led the recent Series E financing round for the company.

The details

The investment, led by Infinitum Partners, consists of both equity and a credit facility. Labelbox will deploy these resources to increase its technical capacity and expand the teams dedicated to its core AI evaluation and agent-based infrastructure products.

Labelbox achieved profitability during the 2026 fiscal year.

The Tech Race

Labelbox aligns its development trajectory with the industry-wide shift toward agentic AI that executes complex tasks rather than simply generating text. This moves the company beyond the initial generation of foundational models toward the infrastructure required for functional, real-world autonomy.

Businesses utilizing these AI agents may see improved performance in automated workflows as the technology gains more robust learning infrastructure. Users of AI-integrated enterprise tools can expect more reliable agents capable of handling increasingly complex real-world tasks.

The takeaway

The move toward profitable, venture-backed infrastructure signals that the AI industry is shifting from pure model development to practical, agent-based implementation. Enterprises looking to integrate AI should prioritize platforms that provide clear evaluation and managed learning pathways.