Labelbox Raises $100M+ Series E for Horizon and AI Agents

The grading rubric is becoming part of the product a company buys from Labelbox. A manager specifying a satisfactory result gives its enterprise agents something concrete to work toward, and a way to carry corrections into the next assignment.

The San Francisco company, led by co-founder and CEO Manu Sharma, announced more than $100M in Series E financing on October 14, 2026. Infinitum Partners leads the package, which combines equity and a credit facility at a $1.25B pre-money valuation. Labelbox intends to expand research, engineering, customer-facing teams and compute for Horizon, its AI training environments, and Recursion, its enterprise-agent platform.

Those products bring Labelbox into two connected purchasing conversations. A frontier laboratory needs tasks that help a model improve; an operating company needs the model to finish work to its own standard. Labelbox is trying to serve both, with human judgment present in the scoring of an exercise and the evaluation of a completed job.

What Labelbox's Series E Financing Includes

The financing announcement names Wipro Ventures, a16z, B Capital, Tau Ventures and Snowpoint Ventures alongside lead investor Infinitum Partners. Labelbox has not disclosed the allocation between equity and credit, the credit provider or how much of the facility has been drawn. The headline amount therefore describes a financing package, with different forms of capital inside it.

The $1.25B figure is the company's valuation before the new equity investment. Computing a post-money valuation would require the equity component, which has not been supplied. The distinction is especially useful when comparing this announcement with rounds in which the entire stated amount purchases shares.

Labelbox says it became profitable in 2026 while growing more than 5x year over year. The announcement leaves the growth measure unspecified and does not supply audited revenue or profit figures. Its plans for the proceeds center on product development, people and compute, with no disclosed budget for each category.

That combination gives the financing a practical operating frame. Labelbox is supporting work for AI laboratories while expanding a product sold to enterprise teams, whose purchasing decisions depend on the work they need completed. The capital can support that expansion; the announcement provides no basis for estimating how quickly those customers will adopt it.

From Training Data to Work That Can Be Scored

Manu Sharma, Brian Rieger and Dan Rasmuson founded Labelbox in 2018. The company initially supplied tools for capturing human expertise in training data, giving AI teams a way to organize the judgments their models needed to learn. Rasmuson is a former Labelbox CTO; Humata's current leadership page identifies him as its founder and CTO.

The funding history traces the earlier business. Labelbox's company-issued Series C announcement disclosed $40M led by B Capital in February 2021. Its January 2022 Series D added $110M led by SoftBank Vision Fund II. Those are historical equity rounds, separate from today's mixed financing package.

The latest product expansion changes what that expertise is used to judge. Horizon supplies reinforcement-learning environments for post-training models on reasoning, tool use and computer use. Its WorldSim system recreates enterprise software and business data so an agent can attempt a task across the systems that task involves. Correctness checks and human preference judgments provide different signals about the resulting work.

An answer can satisfy a mechanical check while still disappointing the person who needs to use it. That gap gives domain expertise a continuing job as models gain more capabilities. For Labelbox, extending the training environment into enterprise software creates another place to apply the experience it accumulated organizing human feedback.

What Enterprise Teams Are Being Asked to Define

Labelbox opened Recursion Managed Agents on October 8, 2026, following internal use. Teams specify a goal, a trigger and criteria for an acceptable outcome. A coordinator assigns work to specialists, and a separate grader examines the evidence against those criteria. Labelbox says the system can revise failed work and retain lessons for subsequent runs.

The current product page describes persistent sessions, scoped tools and a record of the work performed. Those are vendor-described capabilities. The record is commercially useful because it gives the customer a place to examine how an agent arrived at its result, rather than making the delivered file the only thing available to review.

Writing the rubric also exposes decisions a team might otherwise leave implicit. A finance manager may care about the explanation of a changed forecast as well as its delivery date. An engineering manager may need a reproducible defect and a useful fix, alongside a report that looks complete. These are examples of the judgment required to specify work, not claims about named customers or measured deployments.

As assignments recur, the standard itself can change. A team learns what to ask for after seeing the first results, and the supplier has to make those corrections usable in the next session. The product relationship consequently includes the customer's evolving definition of an acceptable job, along with the machinery that executes it.

The Customer Relationship Behind the Expansion

Labelbox's current company profile presents Horizon and Recursion as the two sides of its business. Laboratories buy environments for improving model capability; enterprises buy a system intended to apply that capability to recurring work. Both rely on a useful account of what happened and how the result was assessed, even though their immediate buyers and objectives differ.

The distinction also places Labelbox in a wider enterprise-agent market. DevCuration's coverage of Scuba's shared context for teams and agents examines how organizational knowledge travels into software. Harbor's agent-risk and insurance approach brings evidence of agent behavior into a different commercial conversation. These companies address adjacent needs; the connection is an editorial observation, with no partnership implied.

Labelbox's expansion adds outcome grading to that conversation. Its ability to build simulated work for laboratories supplies relevant technical experience, while enterprise adoption asks the company to understand standards that vary by team and assignment. The customer contributes a definition that has to remain useful after the model, the tools or the underlying business changes.

The financing gives Manu Sharma's team more resources to develop both products. An enterprise customer's next correction is still consequential: it can change the rubric, the evidence the grader asks for and the memory an agent takes into its next job. Labelbox's relationship with that customer continues through those revisions, one assignment at a time.