Town, the San Francisco startup that gives every employee an AI assistant of their own, has raised a $108 million Series B at a $1 billion valuation. The round was co-led by Forerunner Ventures and Menlo Ventures, with participation from existing investors a16z and First Round Capital, plus Better Tomorrow Ventures, Precedent.vc, Scribble Ventures, Serena Ventures, Sparked Ventures and a group of angels that includes Brendan Foody, Josh Silverman, Romain Huet, Scott Belsky, Steve Bartel, Taro Fukuyama and Varun Anand.
The raise comes four months after Town's public launch and takes its total funding to at least $160 million, following a $55 million Series A announced in June. TechCrunch reports that the company reached millions of dollars in annual recurring revenue within weeks of launching.
From personal assistants to teammates
Town's pitch starts with a gap it says current AI tools cannot close. Personal AI assistants have made individuals faster, but most real work depends on other people: a model update from finance, access from an ops lead, input from the field sales team. The faster each person moves, the more they wait on one another. A personal assistant knows its user but cannot work with the rest of the team; a team bot can see shared channels but knows nobody's inbox or preferences.
Town's answer is an assistant per person, called a Townie. Each Townie connects to its person's email, docs, calendar and core work systems, and learns their relationship context, projects and voice. With this launch, Townies can now work directly with each other: they ask each other questions, connect the dots across the company and hand off tasks, so projects stop stalling while people wait for replies.
The company is careful about the trust boundary. Each Townie still works for its own person, shares only what that person would share, asks permission when needed, and escalates the decisions that need a human eye. Townies can also work with people outside the company - customers, partners and candidates - over email and text.
Shared workflows update everyone at once
A second launch feature, shared workflows, lets a team update its assistants collectively. Town's example: when a BizOps team adds new fields to its competitor battle cards, it updates the call-prep process once, and from then on every sales rep's Townie prepares calls with the new competitive analysis built in, with materials refreshed to match. The alternative - each employee re-teaching their own assistant every time a process changes - is the kind of duplicated effort Town wants to remove.
"More people are trusting AI assistants with real tasks, and work is where they need the most help," said Jean-Denis Greze, Town's co-founder and CEO, in the announcement. "Each Townie learns how its person works so it can take on their core work. Townies can ask each other questions and hand off work, so no one is stuck waiting. AI has made each of us faster on our own. Now, it can make teams faster together."
Greze is the former CTO of Plaid. Town itself is two years old and based in San Francisco.
The business angle
Town says each company owns the context its Townies build, turning scattered knowledge into a shared asset that compounds rather than walking out the door when employees leave. On infrastructure, the product routes tasks across models from the major AI labs, picking the best model per task and avoiding lock-in to a single provider. The company says that by default nothing a customer connects is used to train a model, and that it is SOC 2 Type 2 compliant.
The round lands in a crowded market. AI assistants of every shape are raising money right now, and several of Town's backers framed the bet specifically around coordination rather than another single-user helper: the claim is that the next productivity jump comes from assistants that can work an organization's seams, not just its individuals. A $1 billion valuation four months after launch - and investors willing to re-up from the seed and A rounds - suggests the market is at least willing to test that claim quickly.
The investor list reads like a map of the early-stage assistant race. Forerunner and Menlo co-led, with return checks from a16z and First Round Capital, which backed the company earlier, and new participation from Better Tomorrow Ventures, Precedent.vc, Scribble Ventures, Serena Ventures and Sparked Ventures. The angel roster - Brendan Foody, Josh Silverman, Romain Huet, Scott Belsky, Steve Bartel, Taro Fukuyama and Varun Anand - adds operators from marketplaces, design and engineering leadership.
Town is also explicit about where it sits in the stack. Rather than training its own foundation model, it routes each task to the model from the major AI labs best suited to it. That keeps customers from being locked into one lab and lets Town swap in stronger models as they ship, but it also means the company's margins and capabilities ride on suppliers it does not control. The counterweight Town offers is the context layer: the relationship map, project history and voice each Townie builds for its person, owned by the customer company rather than by any model provider.
What to watch next: whether Town's multi-model, no-training-by-default posture holds as usage scales, and whether the assistant-to-assistant model holds up inside large companies where permissions and information barriers get complicated. Town is hiring.