The problem of June AI enterprise deployment automation is the founding thesis of a New York-based startup that emerged from stealth this week with $20 million in pre-seed funding, arguing that the industry’s current answer to AI rollout complexity (hiring armies of specialist engineers) is the wrong one.
The round was led by Marc Benioff’s Time Ventures and drew backing from a roster of technology executives including Michael Dell, Aaron Levie, and George Kurtz. Less widely reported among the named backers is Diane Greene, co-founder and former chief executive of VMware, who also participated, according to the company’s launch announcement. Institutional participants included SV Angel, Conviction Embed, Abstract, and Vesey Ventures, among several other venture firms. June declined to disclose its valuation.
‘AI, paradoxically, increases the demand for professional services,’ says Efrat Rapoport, June’s chief executive and a former Salesforce executive. ‘The industry’s answer to AI implementation is, “let’s hire more and more and more people.”‘
Rapoport and her three co-founders (Ohad Hen, Barak Goldstein, and Idan Tsitiat) have spent years watching that problem compound. The four previously built Bonobo AI, a pre-transformer language model company that launched a voice-to-text service in 2017. An anonymous source familiar with the transaction told Calcalist the deal was valued at approximately $50 million, though a separate aggregator source has put the figure at $45 million; Salesforce did not disclose financial terms publicly. Following the acquisition, roughly 20 Bonobo employees joined Salesforce’s Einstein Forecasting team, then led by vice president of engineering Elad Donsky.
Before founding Bonobo, the team had also attracted backing from L Marks’ Drive programme through a partnership with glass-repair group Belron, L Marks later described the Salesforce acquisition as its first exit of an Israeli company. Bonobo was incorporated as Bonobot Technologies Ltd. and was founded in Tel Aviv.
June AI Enterprise Deployment: What the Platform Actually Does
After several years inside Salesforce, the team left having watched large enterprises struggle to translate AI prototypes into working production systems. The obstacle, Rapoport argues, is not the AI models themselves. It is the infrastructure underneath: duplicate database fields, fragmented data spread across Salesforce, ServiceNow, Workday, and Databricks, and years of accumulated technical debt.
‘Before AI can create value, someone has to deal with legacy systems,’ she says. ‘You have fragmented data across these platforms. You have complex workflows. You have years of technical debt.’
June’s platform begins with process mining, extracting how a business actually operates from the systems it runs on, working interoperably across platforms including Snowflake and Databricks. It identifies bottlenecks and builds optimised, agent-powered replacements, notifying relevant teams automatically through existing communications 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,’ 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 organisation.’
A Mortgage Lender’s Wall, and How June Cleared It
Paul Akinmade, chief strategy officer at CMG, a major US mortgage lender, ran into the problem firsthand. He had moved his company’s software engineering to Claude Code, but hit a wall integrating it with Salesforce, a particular pressure given that he had publicly committed at Salesforce’s annual conference to returning the following year with 100 agents running.
His team spent weeks consulting architects, forward-deployed engineers, and anyone else they could find, without progress. June, he says, gave them a clear view of where to deploy agents and let them move forward safely, even before the two companies had held their official kickoff call.
Akinmade’s terms for any tool he’d consider were blunt. ‘If your product requires FDEs, I don’t want your product,’ he told Rapoport. ‘I’ve already done that and I’m getting annoyed by it. I don’t want a black box. I don’t want something only certain people can figure out. I want an easy-to-use tool.’ June, he says, cleared that bar.
Rapoport frames June as complementary to the forward-deployed engineers and consultants proliferating across the industry, not a replacement for them. Her customers may see it differently. The appeal Akinmade describes is precisely that it lets companies skip those engagements altogether.
The $20 million will go toward scaling the platform’s ability to automate implementation, migration, and adoption of enterprise software, with the aim of cutting technical debt and accelerating AI rollouts for large organisations. The question June now has to answer is whether its process-mining approach can hold up across the full sprawl of enterprise stacks, not just the design partners it has worked with so far, but the hundreds of firms that still haven’t managed to get a single agent into production.
