A Marc Benioff-backed startup thinks AI can solve the AI deployment problem

The modern enterprise is currently facing a peculiar paradox: while artificial intelligence promises to streamline operations, the actual process of integrating these tools has become a logistical nightmare. This friction has birthed an entire industry of "forward-deployed engineers" (FDEs)—specialized consultants who parachute into corporations to manually bridge the gap between AI potential and legacy reality.

Efrat Rapoport, a veteran of Salesforce, believes this reliance on human intervention is a sign of a broken system. "The industry’s answer to AI implementation is, ‘let’s hire more and more and more people,’” she notes. Today, Rapoport and her co-founders—Ohad Hen, Barak Goldstein, and Idan Tsitiat—are stepping out of stealth mode with a mission to automate that very process.

A High-Profile Backing

To tackle the "AI deployment problem," the startup, dubbed June, has secured $20 million in pre-seed funding. The round was led by Marc Benioff’s Time Ventures, with additional participation from a roster of industry titans including Michael Dell, Aaron Levie, and George Kurtz.

The founders are no strangers to the AI space. They previously founded Bonobo AI, a language model pioneer that was acquired by Salesforce in 2019. After spending years navigating the complexities of enterprise AI from within the tech giant, the team recognized that the primary hurdle for their customers wasn't the AI models themselves, but the messy, fragmented infrastructure they were being forced into.

"Before AI can create value, someone has to deal with legacy systems," says Rapoport. "You have fragmented data across these platforms. You have complex workflows. You have years of technical debt."

The "SaaSpocalypse" and the Reality of Integration

While the tech industry frets over whether AI will render software companies obsolete, the reality on the ground is far more mundane. Fortune 500 companies are not simply replacing their CRMs with AI; they are struggling to make AI agents communicate with established platforms like Salesforce, ServiceNow, DataBricks, and Workday.

The challenge, according to Rapoport, is not building an agent template—that is the easy part. The true difficulty lies in the "mess underneath." Enterprises are often plagued by duplicate database fields, conflicting data sources, and siloed teams. An AI agent cannot function effectively if it cannot navigate these internal bottlenecks.

How June Works

June’s platform acts as an automated architect for AI deployment. Instead of relying on a team of consultants to map out a company’s technical debt, June scans the existing ecosystem to identify business processes and bottlenecks.

The platform provides a comprehensive, automated roadmap for deployment:

• System Discovery: Automatically maps existing workflows and data structures.
• Bottleneck Identification: Pinpoints where legacy systems are hindering AI performance.
• Step-by-Step Remediation: Offers clear instructions, such as "remove these duplicates" or "connect to this data source."
• Automated Building: Once a task is verified, users can click "build," and June handles the implementation within the organization.

Real-World Validation

The demand for such a tool is already evident. Paul Akinmade, Chief Strategy Officer at CMG, a major U.S. mortgage lender, found himself in a bind while trying to integrate AI agents with Salesforce. Despite his team’s best efforts and consultations with various experts, they hit a wall.

"My team spent weeks hitting a wall—meeting with architects, talking to forward-deployed engineers, consulting everybody they could—without making progress," Akinmade explained. When he piloted June, the results were immediate. The platform provided the clarity his team needed to deploy agents safely and efficiently.

For leaders like Akinmade, the value proposition is clear: they want to avoid the "black box" of traditional consulting. "If your product requires FDEs, I don’t want your product," he told Rapoport during the pilot phase. "I don’t want something only certain people can figure out. I want an easy-to-use tool."

Key Takeaways

• The Problem: Enterprise AI adoption is currently stalled by legacy technical debt and fragmented data, leading to an over-reliance on expensive, manual consulting services.
• The Solution: June provides an automated platform that maps enterprise systems and builds the necessary infrastructure to support AI agents.
• The Funding: The company raised $20 million in pre-seed capital, backed by high-profile investors including Marc Benioff and Michael Dell.
• The Goal: To move away from the "black box" of forward-deployed engineering and toward a self-service, automated model for enterprise AI integration.

As June moves out of stealth, it faces the challenge of proving that its software can navigate the unique, messy, and highly specific technical environments of the world's largest companies. If successful, it may well provide the missing link that finally allows AI to move from the pilot phase to full-scale enterprise production.