启动 HN: Vela (YC W26) – 用于复杂调度的人工智能

Hi HN! We&#x27;re Gobhanu and Saatvik (brothers), building Vela (<a href="https:&#x2F;&#x2F;tryvela.ai">https:&#x2F;&#x2F;tryvela.ai</a>) - AI agents that handle multi-party, multi-channel scheduling.<p>Scheduling is a constraint satisfaction problem disguised as email! It’s easy when it’s two people, one timezone, one channel. But it becomes a constraint satisfaction problem when inputs are unstructured natural language across multiple communication channels, constraints change mid-solve, and the objective function includes social dynamics that don&#x27;t exist formally anywhere.<p>What if scheduling just happened? For example: a recruiter sends one message, and every interview across five candidates, three hiring managers, and two time zones gets booked, confirmed, and updated automatically. No links, no back-and-forth, no one spending hours with 20 emails. Everyone just gets the right invite at the right time, on whatever channel they actually use. That&#x27;s what we built Vela to do.<p>You loop in Vela into your emails, SMS, WhatsApp, Slack, phone or integrate into an ATS etc and it takes over: reads context, checks calendars, proposes times, follows up when people ghost, and rebooks when things shift.<p>One of our first customers is a staffing firm that searched for a scheduling solution for almost eight years. Their coordinators manage hundreds of candidate-client interviews where each side needs separate email threads, separate Zoom accounts to avoid double-booking links, and calendar invites connecting parties who never directly communicate. A client reschedules one interview and it cascades into four others. A candidate responds on SMS to a thread that started on email. Vela solved this in just 10 minutes of onboarding.<p>The hardest part has been the data problem. Scheduling behavior varies enormously across populations. C-suite folks respond to email within hours and expect formal 3-option proposals. Truck drivers applying for logistics roles respond to SMS at odd hours from shared devices with &quot;y tm wrks.&quot; The failure mode isn&#x27;t parsing -- it&#x27;s applying the wrong interaction pattern for the wrong segment and watching the conversation die. We&#x27;ve been building behavioral datasets from thousands of real interactions: response latency by role, channel preference by demographic, follow-up timing curves, how many options to propose before you hit decision paralysis. This data doesn&#x27;t exist anywhere.<p>The core agent challenge is state across channels. When someone responds on SMS to a thread that started in email, Vela needs to unify identity, merge context, and continue without losing information. Phone numbers don&#x27;t map cleanly to emails, people use nicknames on text, shared devices mean the responder might not be who you reached out to. Temporal NLU is its own problem -- &quot;next Friday&quot; means different things on Monday versus Thursday. We extract structured constraints from natural language and resolve against calendar state. When ambiguity can&#x27;t be resolved, Vela asks -- but deciding when to ask versus infer depends on the stakes of getting it wrong.<p>We&#x27;re live with paying enterprise customers and every client still surfaces edge cases that surprise us. Case studies on our site (<a href="https:&#x2F;&#x2F;tryvela.ai&#x2F;case-studies&#x2F;">https:&#x2F;&#x2F;tryvela.ai&#x2F;case-studies&#x2F;</a>). You can check out a demo here: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MzUOjSG5Uvw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MzUOjSG5Uvw</a>.<p>We&#x27;d love feedback from anyone who&#x27;s worked on multi-agent coordination, conversational AI across channels, or constraint satisfaction in messy real-world domains. Looking forward to your comments!