Town, a San Francisco startup building AI assistants for the enterprise, announced a $108 million Series B round on Monday that values the two-year-old company at more than $1 billion. The round was co-led by Forerunner Ventures and Menlo Ventures, with Andreessen Horowitz, Serena Ventures and First Round Capital also participating. The milestone arrives just four months after Town launched publicly and only weeks after the company said it had reached millions of dollars in annual recurring revenue — a pace that puts it among the fastest startups to cross the unicorn threshold in the current AI cycle.

What Town actually sells

Town was founded by Jean-Denis Greze, the former chief technology officer at Plaid, and it targets businesses rather than consumers. Each employee gets a personal AI assistant called a Townie that connects to email, calendars and other core work systems to help with day-to-day tasks. The assistants are designed to communicate with one another and share information that has been approved for internal distribution, and they can now also interact with customers and other people outside the company. That external-facing capability is a meaningful step: it moves the product from an internal productivity layer into something that touches customer relationships, where accuracy, permissions and auditability matter far more.

The shared-workflow bet

Alongside the funding, Town announced a shared workflow that pushes new business information into employees' Townies automatically, instead of requiring each worker to update their own assistant by hand. The feature addresses one of the persistent weaknesses of personal AI agents: they drift out of date as organisational knowledge changes, and keeping them current is a manual chore that rarely scales. If Town can make context propagation a platform-level function, it removes a significant operational burden for IT and gives the company a defensible reason to sit between employees and the systems they already use.

Funding history and the hype question

The Series B follows a $55 million Series A announced in June, bringing Town's total raised to at least $160 million. A company that was founded two years ago, launched four months ago and is already a unicorn invites the obvious comparison to the frothier corners of the AI market, where valuations have repeatedly run ahead of revenue. Town's counterargument is its traction: millions of dollars in ARR within weeks of launch is unusual for enterprise software, and the involvement of investors such as Menlo Ventures and Andreessen Horowitz signals that people with access to the company's metrics believe the growth is real. The open question is retention. Enterprise AI assistants are easy to pilot and harder to keep deployed once the novelty fades and security reviews begin.

Why the enterprise assistant market is crowded

Town is one of many AI assistants flooding the market, from model providers shipping agentic features to productivity suites bundling assistants into tools employees already pay for. That crowding matters for a startup selling to businesses: buyers increasingly prefer assistants embedded in existing platforms over a separate product that must be integrated, permissioned and maintained. Town's differentiation rests on the Townie-to-Townie communication model and the shared workflow, which together aim to make the assistants a network rather than isolated chatbots. Whether that architecture becomes a genuine moat or a feature that larger vendors replicate is the central strategic question for the company over the next year.

What comes next

With $160 million raised and a unicorn valuation attached, Town now has to convert early revenue into durable enterprise contracts. The company has not detailed how it will spend the new capital, but the pattern for rounds of this size in enterprise AI is consistent: hiring, go-to-market expansion and deeper integrations with the systems its assistants connect to. For technology leaders evaluating AI assistants, Town is now a name that will appear on more shortlists — and a test case for whether rapid revenue growth in AI software translates into the kind of lasting enterprise footprint that justifies a billion-dollar price tag.