Large enterprises are struggling to deploy AI tools reliably, and this challenge has given rise to a new breed of specialists known as forward-deployed engineers (FDEs), who embed within companies to get AI systems operational. But a new startup, June, backed by Marc Benioff's Time Ventures, believes AI itself can address this deployment bottleneck.
The Professional Services Paradox
“AI, paradoxically, increases the demand for professional services,” says Efrat Rapoport, June's CEO and cofounder, who previously served as an executive at Salesforce. “The industry’s answer to AI implementation is, ‘let’s hire more and more and more people.’”
Rapoport and her cofounders—Ohad Hen, Barak Goldstein, and Idan Tsitiat—have a different vision. They’ve raised $20 million in pre-seed funding led by Time Ventures, with participation from tech luminaries like Michael Dell, Aaron Levie, and George Kurtz. The company declined to disclose its valuation. Impressively, Rapoport says the raise was so straightforward that “we didn’t even have a deck for this raise.”
A Team with Deep AI Roots
The founding team previously launched Bonobo AI, a pre-transformer language model company that introduced a voice-to-text service in 2017. Salesforce acquired Bonobo AI two years later, and the team spent several years driving Salesforce’s AI initiatives before leaving to start June, motivated by witnessing customers struggle to integrate AI into their existing platforms.
The Legacy Systems Challenge
While the software industry fears a “SaaSpocalypse” where AI displaces established vendors, no one is yet vibe-coding a CRM for a Fortune 500 company. Any AI model deployed in a corporate environment must still interface with platforms like Salesforce, ServiceNow, Databricks, Workday, and a dozen other data-management systems.
“Before AI can create value, someone has to deal with legacy systems,” Rapoport explains. “You have fragmented data across these platforms. You have complex workflows. You have years of technical debt.”
Building an agent template is the easy part, she says; making it work with the underlying mess is the real challenge. “How does an agent know how to operate when you have 10 duplicate [database] fields that say the same thing, and different teams are using them?”
How June Works
June’s platform scans a company’s existing systems to map business processes, identify bottlenecks, and design more efficient, agent-powered workflows. It then automates the implementation by providing a step-by-step roadmap and building each component through the organization’s communication channels.
“We give you the full roadmap automatically of what needs to happen step by step for you to actually implement this agent successfully in an enterprise environment, which is often very complex,” Rapoport said. “We give you a step by step guide. ‘Remove these duplicates. Connect to this data source.’ And then you click on ‘build’ on each task, and June starts building it for you in the organization.”
Real-World Validation
The approach is already showing promise. Paul Akinmade, chief strategy officer at CMG, a major U.S. mortgage lender, moved his company’s software engineering to Claude Code but encountered integration issues with Salesforce. That proved problematic, especially after he had publicly committed to the platform at Salesforce’s annual conference. June’s platform offered a way to bridge this gap, underscoring the practical need it addresses.
As 2026 unfolds, the enterprise AI landscape is shifting from toy demos to production-grade systems, and the demand for seamless integration tools like June is only set to grow. By automating the deployment process itself, June aims to make AI adoption not just possible, but practical—without the need for armies of FDEs.
via TechCrunch AI