Former Sequoia Capital partner Kais Khimji has debuted Blockit, an AI-powered scheduling startup that lets autonomous agents negotiate meetings on your behalf. The company closed a $5 million seed round led by Sequoia, signaling rare conviction from a top-tier firm backing one of its own alums as founder.
An AI That Brokers Time Like a Human Assistant
Blockit’s pitch is simple but ambitious: instead of sending a link and hoping people pick a slot, its AI agents talk to one another to find a mutually agreeable time, location, and format. Users can loop the agent into an email by CC’ing it or message it in Slack; from there, the bot takes over, negotiating constraints, time zones, and preferences without more back-and-forth.
Preferences are central. Users can tag which meetings are immovable and which are flexible, set working hours, travel buffers, and rules like when it’s acceptable to compress lunch. The agent can even interpret social signals in messages—prioritizing a formal request over a casual ping—reflecting the nuance human assistants apply every day.
Khimji co-founded Blockit with John Hahn, a veteran of Timeful (acquired by Google), Google Calendar, and Clockwise. Their aim: build what they describe as a social network for time, where agents coordinate directly rather than force people to navigate static links and spreadsheets.
Why This Moment Is Different for Scheduling AI
Automation in scheduling isn’t new. Clara Labs and x.ai both chased the dream last decade, only to shut down or pivot. The difference now is the competence and composability of large language models, which can parse intent, reconcile constraints, and carry context across threads in ways earlier systems could not.
The category leader today, Calendly, popularized link-based booking and was last valued at roughly $3 billion. Blockit is wagering the next leap will be agent-to-agent negotiation that eliminates manual triage. Foundation Capital has described this emerging layer as “context graphs”—systems that learn the hidden logic behind daily decisions. Blockit’s rules engine and preference learning are a direct attempt to productize that intuition.
Early Traction, Price Tag, and Who It Targets
Blockit says more than 200 organizations have already adopted the product, including AI startup Together.ai, fintech player Brex, robotics firm Rogo, and venture firms a16z, Accel, and Index. The service offers a 30-day free trial, then charges $1,000 per year for individuals and $5,000 per year for a team license with multi-user support—pricing that clearly targets executives, dealmakers, and operations-heavy teams rather than mass-market users.
Sequoia co-steward Pat Grady characterized Blockit’s revenue potential as nine-figure-plus if execution matches ambition. That kind of endorsement—paired with a lead check—puts Blockit on a short list of workflow AI startups with heavyweight backing from day one.
How It Compares to Big Tech and Legacy Tools
Microsoft Outlook’s scheduling assistant, Google Calendar’s smart suggestions, and Slack-native bots can propose times, but they typically rely on the user to finalize details or send links. Blockit’s differentiation is autonomy: agents broker the meeting end-to-end, reconciling constraints across companies without exposing full calendar details.
This approach also aims to solve the etiquette problem. Link-sharing can feel one-sided; a negotiating agent more closely mirrors how human assistants coordinate, preserving social norms that matter in sales, recruiting, and investor relations.
Trust, Data, and the Enterprise Hurdles Ahead
Winning over enterprises will require clear answers on privacy and compliance—how message content is processed, what is encrypted, how long data is retained, and whether models are trained on customer data. Buyers will ask about SOC 2, data residency, and tenant isolation, especially when agents parse emails to infer urgency and authority.
The upside is significant. Doodle’s State of Meetings report previously pegged the cost of poorly organized meetings at $399B annually in the U.S., a reminder that time fragmentation is expensive at scale. Even modest gains compound: if a team running 50 external meetings a week saves 5 minutes per meeting by offloading negotiation, that’s more than 200 hours a year recovered.
A Bet on Agentic Workflows Becoming the Default
Blockit’s success hinges on whether agentic workflows become normal inside email and chat. If they do, scheduling is a logical beachhead; from there, similar agents could handle follow-ups, travel holds, room bookings, and pre-read distribution. That laddered expansion is likely part of the rationale behind Sequoia’s early bet.
For now, Blockit offers a pragmatic litmus test for AI in the office: can a machine navigate human nuance well enough to give you hours back without creating new friction? If early adopters stick and the negotiation feels truly hands-off, the calendar might be where AI quietly proves its worth.