BAG Ventures has closed its first venture fund at $11.3 million. The firm was founded by two former Google executives, Bonita Stewart and Jackson Georges Jr. The new fund will back early-stage artificial intelligence startups that aim to solve real problems for large businesses.
The fund comes at a time when many companies are testing AI tools but want clear proof that these products can create business value. BAG Ventures believes the next phase of enterprise AI will focus less on simple experiments and more on products that can do real work inside a company.
The firm has already backed 10 companies. Its portfolio includes SXD, BizTrip, and Nomadic. BAG Ventures plans to continue its focus on AI companies that can become part of major business systems and show a clear path to revenue.
Who Founded BAG Ventures
Bonita Stewart spent 17 years at Google, with almost 10 years as a vice president. During her time at Google, she also served on the board of Gradient Ventures, Google’s early-stage AI fund.
Her experience gave her a close view of how large technology companies build products and work with new businesses. She is also a limited partner in the Female Founders Fund and the Operator Collective.
Jackson Georges Jr. also has experience at large companies and investment firms. He worked at GE Healthcare and later joined Google. He then became a partner at CapitalG, the growth investment arm of Alphabet.
Stewart and Georges also worked together on BAG Collective, an angel investor group with more than 450 members. Their work with that network helped them build links with technology executives and other business leaders.
The two founders say this network is one of the main strengths of BAG Ventures. They want to give startup founders more than money. They also want to help those founders meet possible customers and business leaders.
Why the $11.3 Million Fund Matters
The $11.3 million fund is not one of the huge venture funds now seen across the AI sector. However, BAG Ventures has a very specific goal.
The firm wants to find early-stage AI startups that can sell to businesses and show real value.
This approach comes from the founders’ view of the current AI market. Many companies first tested AI through small pilot projects. Some bought chatbot tools or gave employees access to general AI systems.
BAG Ventures believes that phase is now changing.
Jackson Georges said companies are paying closer attention to unit economics. In simple terms, businesses want to know what they get for each dollar they spend on an AI product.
A company may be interested in a new AI tool, but interest alone is not enough. It must also show that the product can solve a real problem, save time, reduce costs, improve a process, or create some other clear business result.
That is the type of startup BAG Ventures wants to find.
From AI Experiments to Real Business Work
The founders believe enterprise AI is moving away from what they describe as an “experimental sandbox” phase.
In the early phase of AI adoption, companies often tested tools without a clear long-term plan. A team might try a chatbot or an AI assistant simply to see what it could do.
BAG Ventures believes businesses now want more focused products.
For example, a company may not want a general chatbot. Instead, it may want an AI system that can review software code, read legal documents, handle a specific customer process, or complete another defined task.
The difference is important.
A general tool may help a worker with many small tasks. A focused AI system can become part of a business process and complete a specific job.
Georges expects this type of AI to gain more attention as companies become more careful about their technology budgets. He believes businesses may eventually pay for completed jobs and outcomes, rather than simply pay for access to software on a per-user basis.
A Focus on Early-Stage Startups
BAG Ventures focuses on early-stage AI companies.
The firm writes checks of between $100,000 and $500,000. It plans to deploy the rest of its fund over the next two years.
The firm looks across several parts of the AI market. These include AI infrastructure, compute, physical AI, edge AI, security, governance, and vertical SaaS.
This gives BAG Ventures a wide view of enterprise AI while keeping its main focus on business use.
The firm is not limited to one industry. Instead, it wants to find companies where AI can become a core part of a business process.
Its existing portfolio gives an idea of this approach. The firm has backed SXD, an enterprise software company; BizTrip, an AI travel agent; and Nomadic, an agentic reasoning platform.
What BAG Ventures Wants From Founders
The firm has a clear set of expectations for the startups it considers.
BAG Ventures wants technical teams that have already worked together. It also wants companies with a minimum viable product and at least one partner.
Another major requirement is a clear path to revenue.
Georges said the firm wants startups with a “very clear path to monetization within 24 hours.”
The idea is simple. A startup should not only have interesting technology. It should also have a product that can solve a problem for a customer and create a clear reason for that customer to pay.
This approach puts a strong focus on business use from an early stage.
For founders, that can create a different type of investor relationship. Instead of only asking how advanced the technology is, the investor may also ask who will buy it, why they will buy it, and how soon they can pay for it.
Customer Access Is a Key Part of the Fund
BAG Ventures says its main advantage is its access to senior business operators.
The founders say many startup teams can build good products but have trouble reaching the people who can buy those products inside large companies.
At the same time, many senior executives want to help young companies but do not always have a direct way to connect with them.
BAG Ventures wants to bring those two groups together.
The firm says it gives founders capital as well as direct introductions to possible customers and hands-on help with go-to-market work.
Its limited partner network includes people from major companies such as Google, Nvidia, Amazon, and Snowflake. The firm has more than 150 limited partners in total.
This network could be useful for a young company that needs its first major enterprise customer.
A Large Operator Network
The firm’s connection to BAG Collective is also important.
BAG Collective has more than 450 members. The group brings together technology operators and business leaders.
For a startup, such a network can provide access to people with experience in areas such as sales, product development, security, technology, finance, and business operations.
That can matter as much as funding for an early company.
A startup may have strong technology but still need help with pricing, customer access, hiring, product plans, or enterprise sales.
BAG Ventures wants its network to help with those problems.
The firm’s founders believe this can give portfolio companies a stronger link to large organizations.
The Search for Deep Enterprise Products
BAG Ventures does not want to invest heavily in products that simply place a thin layer on top of a large AI model.
The firm believes such products may face a serious challenge when major AI companies release new models or add similar features to their own platforms.
For this reason, BAG Ventures looks for startups that go deeper into enterprise systems.
The firm wants companies that connect closely with business workflows and build access to proprietary data.
This type of data can be difficult for competitors to copy.
A startup may become more valuable if its product learns from unique company information, becomes part of daily work, and creates a strong reason for customers to stay.
The founders believe this can create a stronger business than a product that simply calls an outside AI model through an API.
Why Proprietary Data Matters
Data can become an important advantage for enterprise AI companies.
Large businesses have years of internal information. This can include contracts, customer records, product details, technical documents, internal processes, and other private material.
A startup that can work safely with this data may offer something that a general AI model cannot easily provide.
BAG Ventures wants to back companies that become part of these deeper workflows.
The goal is not simply to give employees another AI tool. The larger goal is to create software that becomes part of how a company works.
If a product handles a key task every day, replacing it can become difficult.
That can give the startup stronger customer retention and a more stable business model.
Security Is Another Major Area
Security is also a major focus for BAG Ventures.
Large companies cannot freely send private business information into every AI system. They need controls around data access, privacy, and acceptable AI use.
The firm is therefore interested in startups that work on security, governance, and identity for AI systems.
Georges said regulated industries will have a strong need for secure data flows and clear rules for AI use.
He also pointed to continuous automated red-teaming as one area that could become more important.
Red-teaming means testing an AI system to find weaknesses before those weaknesses create a larger problem.
BAG Ventures says this approach is already used by its portfolio company Defendremate.
AI Agents Create a New Security Problem
The rise of AI agents creates another issue.
A normal employee has a name, an account, and specific access rights inside a company’s systems.
An AI agent may also need access to those systems.
That creates questions about identity and permission.
For example, an AI agent may need to read company documents, access a database, create a report, or complete another task. The company needs to know what that agent can access and what actions it can take.
BAG Ventures believes startups that solve this problem could have a large market.
Georges has pointed to the need for Identity and Access Management for non-human workers, such as AI agents.
He also sees an opportunity for a new form of zero-trust architecture built for agent-based systems.
Zero trust means that a system does not automatically trust a user or device. It checks access based on identity, rules, and the task at hand.
As AI agents gain more access to company systems, these controls may become more important.
The Enterprise AI Market Is Changing
The launch of BAG Ventures comes during a major shift in the enterprise AI market.
Large companies now have more AI products to choose from. They can buy general-purpose tools from major technology companies or use products from smaller startups.
This gives businesses more choice.
At the same time, it makes life harder for startups.
A young company cannot always compete with a major technology firm on the size of its AI model. It may need to compete through a strong product, a special business process, unique data, or close customer relationships.
That is the type of startup BAG Ventures wants to support.
The firm’s strategy is based on the idea that enterprise AI will reward products that do specific work rather than products that only provide access to another chatbot.
The Next Two Years
BAG Ventures has already invested in 10 companies and plans to put the rest of its $11.3 million fund to work over the next two years.
Its checks of $100,000 to $500,000 allow the firm to support several early-stage companies rather than place the full fund into only a few businesses.
The firm can also use its large operator network to help those companies find customers and improve their go-to-market plans.
For founders, this means the fund’s value may come from more than the amount of capital it provides.
Its founders have spent years inside large technology companies and investment firms. Their experience gives them a view of how large enterprises buy technology and what can stop a startup from gaining a customer.
A Different Kind of AI Investment
BAG Ventures is entering a crowded AI investment market, but its focus is clear.
The firm wants to find startups that can show a direct connection between AI and business results.
Its $11.3 million Fund I is aimed at early-stage companies across AI infrastructure, compute, physical and edge AI, security, governance, and vertical SaaS.
The fund has already backed 10 companies, and its check sizes range from $100,000 to $500,000.
The firm also has more than 150 limited partners, including people connected to Google, Nvidia, Amazon, and Snowflake. Its wider BAG Collective network has more than 450 members.
What This Means for AI Startups
For AI founders, the message from BAG Ventures is straightforward.
A strong AI model alone may not be enough.
Startups may need a real product, a real customer, useful enterprise data, and a clear way to make money.
They may also need to fit into existing business systems instead of asking companies to create an entirely new process.
This is especially important as major AI labs continue to improve their own models and add new features.
A startup that depends only on access to one model may face pressure when a larger company offers a similar feature.
A startup that owns a deep workflow and has strong customer relationships may have a different type of protection.
BAG Ventures’ Enterprise AI Bet
BAG Ventures has raised $11.3 million to support a group of early-stage AI companies that aim to solve real enterprise problems.
The fund comes from two people with deep experience at Google, CapitalG, and other technology organizations. Their strategy places a strong focus on customer access, enterprise workflows, proprietary data, security, and clear paths to revenue.
The firm has already backed 10 startups, including SXD, BizTrip, and Nomadic. It plans to use the remaining capital over the next two years.
Its thesis is based on a simple idea: businesses will continue to use AI, but they will become more careful about what they pay for.
The companies that can prove clear value may have a stronger case for long-term enterprise adoption.
For BAG Ventures, the next stage is now about finding those companies early and helping them reach the large customers that can turn a promising AI product into a real business.
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