Rox Raises $100M Series B at $1.3B Valuation to Fund AI Agent Research

Rox has raised $100 million in a Series B round that values the San Francisco-based startup at $1.3 billion, Reuters reported on November 11, 2026. General Catalyst and March Capital co-led the financing, and the company plans to spend the money on AI research and custom-model development while expanding its enterprise reach.

The raise matters because it puts fresh capital behind a company whose annual recurring revenue grew more than tenfold during its first year on the market — a growth rate that suggests enterprises are paying for AI sales agents, not just piloting them. It also extends a bet General Catalyst already made: the firm led Rox's Series A, announced on November 19, 2024.

Who put in the money?

The round brings together a mix of returning and new backers. The composition signals where investors think Rox is heading — from a sales tool toward a research-driven model company.

• Co-leads: General Catalyst and March Capital
• Returning investors: Sequoia Capital and Factory
• New investors: Databricks Ventures and Prosperity7 Ventures

Databricks Ventures' participation is worth noting: the data-and-AI platform company investing in Rox points toward tighter links between enterprise data infrastructure and agent deployment. General Catalyst, meanwhile, is doubling down after leading the Series A.

What does the platform actually do?

Rox is pitching more than outbound lead generation. Its platform documentation describes one connected system covering prospecting, qualification, engagement, closing, renewal and account expansion. That scope means a single deployment can handle both new-business development and revenue from existing accounts.

The platform draws on a wide set of enterprise sources: CRM records, email, calendars, Slack, call transcripts, documents and internet sources. A knowledge graph resolves entities and conflicting values across those sources and infers relationships between them, while a governance layer applies one access model across systems. The goal is to give agents a shared account context instead of leaving them to work from isolated sales records.

The mechanism matters here. Sales data usually lives in fragments — a contact in the CRM, a conversation in Slack, a contract in a document store. Rox's answer is to fuse those fragments into a single graph so that every agent works from the same picture of an account.

How does the Outbound Agent work?

Before a contact enters an outreach workflow, the Outbound Agent documentation says the system validates the person's current role and email address, supplementing its records with data from more than 17 enrichment providers. Search results carry a matched or rejected label with reasons for exclusion. Approved matches can be transferred to an outreach sequence, contact list or lead list, and the agent can also handle responses and book meetings.

Human control is built in. A team can keep every send under manual review, pause autopilot and inspect booked meetings. Changes to a running agent remain drafts until the user saves or discards them. Rox also states that its automation operates within user-defined parameters and requires initial configuration and continuing oversight.

That framing reads as a deliberate answer to the trust problem in AI sales automation — companies want agents that book meetings, but not ones that email prospects without supervision.

What results have customers reported?

Rox's customer-story index lists several named deployments, and the numbers cover four different dimensions of value rather than one:

• MongoDB took 45 days to move from a prototype into production.
• Ramp's conversion rate rose by 10% to 15%.
• Couchbase reached adoption exceeding 90% among more than 250 representatives.
• Puma Energy uses Rox to monitor customers in key markets.

Those examples span deployment speed, sales conversion, workforce adoption and account monitoring. Rox's existing customer base also includes Puma and the chipmaker Groq.

The company cautions that these performance figures reflect individual configurations and may not represent other deployments. Actual results can vary with data quality, CRM configuration, outreach volume, market conditions and target audience, so the examples do not establish a standard outcome for new customers. That disclaimer is standard practice, but it also frames the central question for a company now selling into five industries at once.

Where will the research money go?

The planned research spending fits an agenda Rox has already laid out on its research page. The work includes:

• Task-specific models and agent architectures
• Systems for evaluating agent behavior
• Production infrastructure intended to make agents reliable in real-world revenue environments

Custom-model development therefore sits within a broader engineering program rather than standing alone as a model-building exercise. Rox is not trying to compete with frontier labs on general models; it is building models tuned for revenue workflows and the evaluation systems to check that they behave correctly.

Who runs Rox?

The company's page identifies four cofounders. Ishan Mukherjee serves as CEO, Shriram Sridharan as CTO, and Diogo Ribeiro as CGO. Christopher Ré, the Stanford researcher, is cofounder for research — a presence that connects the company's model-building ambitions to an established academic track record in machine learning.

What does the Series B signal?

General Catalyst's history with the company adds context to the new round. When the firm announced the Series A on November 19, 2024, it said more than 35 enterprise sales teams had adopted the product during its public beta, including MongoDB, Confluent and Ramp. Two of those beta customers — MongoDB and Ramp — now appear among the named customer results, which suggests some continuity between early adoption and production use.

The Series B gives Rox capital to pursue model development and a larger enterprise footprint at the same time. It is targeting healthcare, the industrial sector, financial services, energy and technology — a wide range of industries with very different data environments and compliance demands.

The central test, as the source itself frames it, is whether Rox's early revenue growth and individually reported customer outcomes can be reproduced across that wider range of industries. A tenfold first-year ARR climb from a small base is one thing; sustained growth across regulated sectors like healthcare and financial services will be the harder proof.