Vela (YC W26) – AI for complex scheduling

Hi HN! We're Gobhanu and Saatvik (brothers), building Vela (https://tryvela.ai) - AI agents that handle multi-party, multi-channel scheduling.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't exist formally anywhere.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's what we built Vela to do.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.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.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 "y tm wrks.

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FL score

77

out of 100

Verdict

BUILD

high confidence

Competition

16

competitors found, emerging market, funded players

Trend

8 community mentions

An AI agent for complex, multi-party, multi-channel scheduling using unstructured natural language, primarily for industries like staffing.

The pain

Complex scheduling is a massive time sink and operational bottleneck for businesses, especially when it involves multiple parties, diverse communication channels (email, SMS, WhatsApp, Slack), unstructured language, and cascading reschedules. Existing tools are too generic or rigid to handle this complexity, leading to hours of manual back-and-forth.

The gap

While many scheduling tools exist (Calendly, Chili Piper, etc.), none fully automate multi-party, multi-channel scheduling that processes unstructured natural language and adapts to social dynamics. Vela's 'AI agent that just takes over' approach across all communication modes fills a specific whitespace for companies with high-volume, complex scheduling needs.

Build angle

Develop AI agents that integrate across email, SMS, WhatsApp, Slack, and potentially ATS, to autonomously read context, propose times, handle follow-ups, and manage reschedules based on unstructured natural language and calendar availability, starting with the specific pain point of staffing firm interview coordination.

Strengths

  • Addresses an extremely painful and specific problem with high severity.
  • Clear market gap for a truly autonomous, multi-channel AI scheduling agent.
  • Strong signals of willingness to pay (8-year search, 10-minute onboarding success).
  • High value proposition for businesses saving significant operational time and cost.
  • Future-proof concept aligned with AI automation trends.

Risks

  • Extremely high technical complexity for a solo builder, especially with natural language processing and multi-channel integration reliability.
  • The 'data problem' of scheduling behavior varying enormously poses a significant challenge for AI agent robustness.
  • Crowded general scheduling market means clear differentiation and proof of AI capabilities are crucial.
  • Achieving robust performance across diverse, unstructured inputs is difficult and prone to edge cases.
  • Creator fit for a solo builder is low due to the complexity.

Next steps

  1. Deeply validate the willingness to pay and exact pain points with 10-20 more potential customers, beyond the initial staffing firm, to ensure the problem is widespread and not an outlier.
  2. Prioritize the core communication channels and the narrowest, highest-impact use case (e.g., email + SMS for recruitment) for an absolute MVP.
  3. Explore existing AI/NLP APIs and orchestration layers to reduce build complexity, rather than building everything from scratch.
  4. Focus on a specific, measurable outcome (e.g., 'reduces scheduling time by X% for Y role') and track it with early adopters.

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