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Founders of BAG Ventures Raise $11.3 Million to Fund AI Companies Targeting Enterprise Spending

BAG Ventures closed its first $11.3 million fund to invest in artificial intelligence startups, focusing on products that demonstrate economic viability and embed themselves deeply in enterprise workflows. The fund aims to have already financed 10 companies, including SXD, BizTrip, and Nomadic, with new investments ranging from $100,000 to $500,000.

2026-09-30
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certi.news Editorial Team
Founders of BAG Ventures Raise $11.3 Million to Fund AI Companies Targeting Enterprise Spending

BAG Ventures announced the closing of its first $11.3 million investment fund to finance early-stage artificial intelligence companies, betting that enterprises are moving from testing general-purpose tools to buying products that perform specific tasks and demonstrate their economic return.

Bonita Stewart, a former vice president at Google, and Jackson Georges Jr., a former partner at CapitalG, established the fund after the duo began investing from it about two years ago, coinciding with the completion of its capital raise. BAG Ventures has so far invested in 10 companies, including software company SXD, AI-powered travel agent BizTrip, and agentic reasoning platform Nomadic.

Investment Focused on Enterprise Use

Investment areas include artificial intelligence infrastructure, computing capabilities, physical and edge AI, security, governance, and vertical software as a service. Individual investments range from $100,000 to $500,000, while the company plans to invest the remainder of the fund over the next two years.

BAG Ventures says its advantage is not limited to capital, but also includes introducing founders to potential customers and providing practical go-to-market guidance. The fund’s limited partners include Google, along with executives from Nvidia, Amazon, and Snowflake, while their total number exceeds 150 investors from multiple companies.

What Is Changing in Practice?

Georges believes enterprises are emerging from the “experimentation environment” in which they openly tested chatbots and artificial intelligence solutions. According to his view, companies are scrutinizing unit economics and paying for deterministic solutions that integrate with legacy work systems and perform clear tasks, such as automating code reviews or analyzing legal documents.

Georges envisions a shift from buying per-user software licenses to paying for the completion of tasks and outcomes produced by multi-agent workflows. For this reason, BAG Ventures looks for technical teams that have worked together before, have an initial product and at least one partner, and a clear path to generating revenue within 24 months.

Depth and Data as Barriers to Competition

The company prefers products that penetrate deeply into enterprise workflows and collect proprietary data that cannot be easily extracted, warning that startups limited to a thin layer over advanced artificial intelligence model interfaces may struggle to withstand the launch of competing products by major labs.

BAG Ventures also monitors companies serving regulated sectors, particularly in protecting internal data flows, establishing acceptable-use controls, and conducting continuous automated penetration testing. The company said this approach is evident in one of its portfolio companies, Defendermate.

BAG Ventures also expects growing demand for identity and access management tools for non-human workers, such as artificial intelligence agents, as well as “zero-trust” architectures and orchestration layers designed for agentic systems.

certi.news Analysis

The fund’s importance lies not only in the amount of financing, but also in its selection criteria, which reflect enterprises’ increasing scrutiny of artificial intelligence’s viability. However, this thesis remains an investment view from BAG Ventures and is not evidence of a comprehensive shift in purchasing behavior. The source also did not clarify the performance of the 10 companies or the revenue they generated, so the ability of this approach to predict winners in a rapidly changing market remains an open question.

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