Enterprise artificial intelligence startup June has emerged from stealth with $20 million in pre-seed funding and a platform designed to help large companies move AI agents from experimental pilots into everyday operations.
The funding round was led by Time Ventures, the investment firm associated with Salesforce co-founder Marc Benioff. The company also attracted backing from technology leaders including Dell Technologies founder Michael Dell, Box chief executive Aaron Levie and CrowdStrike chief executive George Kurtz.
June has not disclosed its valuation.
The startup is entering the market as businesses increase spending on generative AI but continue to face difficulties connecting new models and agents to existing software, databases and internal workflows.
June was founded by Efrat Rapoport, Ohad Hen, Barak Goldstein and Idan Tsitiat.
The four entrepreneurs previously built Bonobo AI, an early language technology company that introduced a voice-to-text service in 2017. Salesforce acquired Bonobo AI approximately two years later, bringing the founders into the enterprise software company.
After spending several years working on Salesforce’s AI initiatives, the team decided to launch another company. Their experience showed them that many businesses were able to build AI demonstrations but struggled to deploy those systems across complex corporate environments.
The founders secured June’s $20 million financing without using a traditional investor presentation, according to Rapoport. That level of investor interest reflects growing demand for tools that can close the gap between AI experimentation and production deployment.
Building an AI agent is only one part of an enterprise deployment. The agent must also connect with software platforms such as Salesforce, ServiceNow, Workday and Databricks, while following the organization’s security policies and operational processes.
Corporate systems often contain years of accumulated technical debt. Information may be spread across several databases, and different departments may record the same information in different ways.
A company could, for example, have 10 database fields that appear to represent the same customer detail. Different teams may use those fields for separate purposes, making it difficult for an AI agent to determine which information is accurate.
June’s platform scans a company’s existing systems to map its business processes, locate operational bottlenecks and identify the changes required before an AI agent can work reliably.
The software then produces a step-by-step deployment plan. Recommended actions may include removing duplicate data, connecting an additional information source or redesigning part of an existing workflow.
Users can approve individual tasks through the platform, allowing June to begin implementing the required changes.
June is built around the idea that artificial intelligence can automate some of the professional services work currently required to deploy enterprise AI.
Large organizations frequently rely on consultants, systems integrators and forward-deployed engineers to configure AI products. These specialists work directly inside customer organizations to connect software, prepare data and adapt technology to existing systems.
June does not position its platform as a complete replacement for those professionals. However, automating parts of the implementation process could reduce the number of specialists required and shorten deployment timelines.
This could be particularly important for companies trying to introduce tens or hundreds of AI agents. Manual integration work becomes increasingly expensive as each agent needs access to different data, software and approval processes.
CMG, a major mortgage lender in the United States, is among the companies testing June’s technology.
CMG chief strategy officer Paul Akinmade had publicly set a goal of deploying 100 AI agents. His engineering team adopted Anthropic’s Claude Code relatively quickly but encountered problems when attempting to integrate AI capabilities with Salesforce.
The company reportedly spent several weeks consulting architects and forward-deployed engineering specialists without finding a clear path forward.
June’s platform helped the team identify where agents could be deployed and what system changes were needed. CMG was able to begin using parts of the technology even before the companies held their formal kickoff meeting.
The case illustrates the problem June hopes to solve. A company may have access to capable AI models and skilled software engineers, yet still be unable to deploy agents because of fragmented data and complicated legacy systems.
The next phase of enterprise AI competition is increasingly focused on implementation rather than model access.
Many companies can now obtain advanced language models through cloud services or application programming interfaces. The more difficult challenge is turning those models into dependable systems that can work with confidential information, follow internal rules and complete tasks across multiple software platforms.
June is betting that businesses will pay for an automated implementation layer that can examine their technology environments and prepare them for AI agents.
With $20 million in initial capital, four founders with previous acquisition experience and support from several prominent technology executives, June has the resources to test that theory across large corporate customers.
Its success will depend on whether the platform can handle the complexity of real enterprise systems while remaining simple enough for internal teams to use without relying on a large group of outside specialists.
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