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Launch HN: Vela (YC W26) – AI for complex scheduling
by Gobhanu
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." The failure mode isn't parsing -- it's applying the wrong interaction pattern for the wrong segment and watching the conversation die. We'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't exist anywhere.
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'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 -- "next Friday" means different things on Monday versus Thursday. We extract structured constraints from natural language and resolve against calendar state. When ambiguity can't be resolved, Vela asks -- but deciding when to ask versus infer depends on the stakes of getting it wrong.
We're live with paying enterprise customers and every client still surfaces edge cases that surprise us. Case studies on our site (https://tryvela.ai/case-studies/). You can check out a demo here: https://www.youtube.com/watch?v=MzUOjSG5Uvw.
We'd love feedback from anyone who's worked on multi-agent coordination, conversational AI across channels, or constraint satisfaction in messy real-world domains. Looking forward to your comments!
How does this compare to solutions like e.g. Clara[0] that have been around for a decade?
A lot of similar solutions came up in the early chatbot era, when Facebook published Ducking and it became trivial to parse dates from natural language. I also looked into building such a product in the time, but ultimately found it hard to find an entry to the market: Most people that actually need something like this do have secretaries (who will also schedule a lot of other things in regards to the meeting) and most other people that have a less severe form of that problem rarely want to actually pay for such a product.
Great question! We have a lot of friends in the b2c space. What Vela is designed for is the subset of scheduling where nothing in the market works, specifically for businesses. Think a staffing firm coordinating across candidates, clients, recruiters, and client development to schedule interviews/meetings. Or another one doing 1,000+ interviews a week, wrangling across phone, SMS, and email. These are scenarios where companies tried every tool out there and eventually just did it themselves because tools couldn't meet their customers where they are and didn't handle the workflows/behaviors of their industries.
I really like the framing of the case studies, the emphasis on Vela taking over their current process rather than requiring any change is very nice. That said, the case studies are interesting in that they reveal that the problems these clients were trying to solve aren’t really scheduling. The employment agency needs parties hidden on invites, the venture fund doesn’t want clients to have to click buttons. The “complex scheduling” doesn’t seem that complex at all, automated reminder calls and sms have been around since Twilio made it possible. I’m interested to see how things pan out for Vela, it feels more destined to be an agency that builds out enterprise scheduling systems for esoteric enterprises, than a scheduling software business. Although that’s not a bad business to be in!
Absolutely! That's how we view scheduling as a problem as well. Much larger than finding times on calendars and more about coordination of systems and people.
> 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.
My very strong advice would be to pick one of these use cases and niche hard. Multi channel, multi party scheduling isnt a problem anyone thinks they have (even if they actually do). They wake up thinking they have 40 truck driver shifts to fill tomorrow.
Deputy cleaned up by going after rota scheduling for independent coffee shops. Logistics sounds like a great shout. Each have messy edge cases which you can develop a strong solution around but you'll get crushed trying to go horizontal in this space. Best of luck!
Thank you for the advice - really appreciate it.
Was actually chatting with a large industrial staffing firm and they were saying the same thing that it was super painful to schedule 1000s of workers for drug tests and then shifts too!
Congrats on this and I do hope you do well, but a polite critique if I may.
How is this better than spending 2-5 mins making a poll and letting people vote?
https://doodle.com has been around forever and doesn’t cost anything.
Thank you! Awesome question. There are a few factors at play here.
One is friction on the other side. With Doodle you're asking someone to click a link, open a UI, parse a grid of times, check boxes, and sometimes connect an account. That's a real ask, especially for someone external who has no relationship with the tool. With Vela they just reply "Tuesday works" in the thread they are already in.
But beyond reducing friction, Vela is also doing the actual coordination work: herding people, following up with non responders, suggesting specific times that work best (not just available ones), handling rescheduling, and closing the loop. It's closer to what a human coordinator does than what a poll does.
Our customers are mostly folks coordinating 30+ meetings a week across multiple people. For them, time spent compounds significantly.
Doodle is great too btw, but it really only works well when the people involved already know each other and at a small scale. Vela is built for the more complicated scenarios where companies have tried everything and decided nothing works but putting a team member on the job.
Do they just say “Thursday works” into the air? Or
Do they have to click a link, open a tool, make an account, and work out where to type and what they’re allowed to say / what the chat bot will understand? And THEN say Thursday works?
Still trying to be polite but frankly a little surprised by your blind spots.
I think the way its supposed to work is the agent (AI) will email the recipients saying "Bob is available Thursday at 8:00 AM or Tue at 9:00 AM"
Then the recipients can reply to the email thread with "Thursday works".
Not affiliated with vela - just what I understand from their site and the comments on this page.
Hey! Fellow YCer (S24) here. Super cool idea. Depending on how b2c you want to be, one area to maybe consider would be surgeries. Scheduling rooms for surgeries is quite challenging, and has a cost component associated with it which makes the problem even harder. Especially since, as you can imagine, it's not at all obvious how long a procedure will necessarily take, and other procedures may need to start at a certain time.
Thank you! That's very interesting and something we are going to double click on. Are you referring to scheduling within a hospital or across hospitals?
Good catch. Cancer treatment scheduling is hard as well as mixes need tombe prepared in advance and cancelles appointments are hard to fill.
if you dont mind me asking what makes the appointments hard to fill? as I understand it the demand far outweighs the supply?
Comment was deleted :(
Really cool! During my university years I had a lot of fun with scheduling 200 interviews for different 20 companies for a career fair.
Created a problem statement and then solved it with Gurobi, repo here: (https://github.com/aleda145/interview-scheduling-kontaktsamt...)
Agents feel like the perfect fit for the whole rescheduling loop that happens in the real world!
Have you had to use an optimization solver yet? If so, which one?
Also "vela" means "to be undecided" or "to go back and forth" in Swedish, great fit!
wow what a wonderful coincidence
I work in tech for Executive Search, which is often (way) lower volume than generalist recruitment, but scheduling is still an issue. Keeping an eye out on this - best of luck.
Thank you very much! We are actually already working with some of the largest US executive search firms (don't have a case study out yet), solving for that exact 'high-touch' scheduling hurdle. If you are ever curious, we would love to share more: https://tryvela.ai/book-demo
this is cool – congrats on the launch.
generally when i give someone my calendar link, i'm pretty happy for them to just choose whatever time within those constraints. i like the future where everyone opts in ("i will meet as long as my preferences are considered") & there doesn't need to be any manual clicking/coordination whatsoever.
as a tidbit of feedback: are you explicitly targeting b2b? i would like to just sign up, but i'll book a demo if that's the only option :)
we are b2b focused unfortunately
but there are a bunch of b2c options :)
Lot of puffery in this describing constraint and actual messy problems that you are all most likely just being thrown into the context for an llm agent... None of the case studies demonstrate complex scheduling at all and are just all individual serial threads. buffers, preferences and options are all simple. The hard part of scheduling is when you have multiple pending invites or invitations that have to resolve and track it down, if someone asks for a meeting on a day that you currently already have a pending invite for, and how far away that day is, and how important the relationship is etc...
The concurrent resolution problem you're describing is exactly what we deal with. When a staffing coordinator has 15 interviews to book across shared interviewers, confirming one cascades into others. We track pending holds, rank by urgency, and when a confirmation on one thread invalidates a proposal on another, Vela detects the conflict and re-proposes. Theres
The only other alternative is a booking link but this, slows down business, doesnt work in many many real life situations and more :)
Fair feedback that the case studies don't show this well - they're simplified to demonstrate the flow. The multi-party dependency resolution is happening underneath but we could surface that better.
On the LLM point - agreed that context window alone doesn't cut it. The coordination and state management layer sits outside the model. We learned that the hard way early on.
have built in this space which led me to develop a minizinc mcp server [0] for scheduling bocce tournaments [1]. scheduling with constraints is a np hard problem and it makes sense people struggle. tools exist to solve this problem but they are complex and hard to use for non technical folks, and even technical folks. am hoping a tool like this can bridge the gap and would like to bring it to your awareness if you aren't already thinking about the problem this way :)
edit: after reading a bit more of description looks like yall are taking a similar approach, kudos!
This is awesome! Completely agree: modeling each real life scenario as a constraint satisfaction problem is tricky in and of itself (especially with the diversity of non-intersecting constraints we encounter) and something we are actively working on. Using LLMs as a layer above has made it much more tractable. Curious how the bocce scheduling has fared in real world scenarios. How was the performance?
Does this work with my OpenClaw?
open claw breaks so often with scheduling... given the number of things that need to be considered to get it right
We do not have a skill if thats what you meant?
out of curiosity how were you planning on using it?
Hello! Not commenting on content or functionality. Scheduling in AI is a very dense field. An a past researcher in AI decision making, I got confused by the 'Scheduling solved' slogan. FYI recent AI for scheduling include GNNs and RL applied to NP and P-space problems that plague many industries. A larger scope I believe from vela's (rightful) target, a bit confusing IMO. Good luck with your endeavor, all scheduling problems are beautiful :)
very fair callout - and I can see how "scheduling solved" reads very differently to someone coming from the optimization/planning side of AI. You are right.
Appreciate the note on the slogan, definetly thinking of revamping our landing page in the near future to speak more directly to our audience.
We’ve been building with a B2C focus for the last several months, our agent is called Meet-Ting, let’s catch up soon - been meeting with all the calendar founders since we started and it’s been really rewarding. Find us on LinkedIn or web.
I like the name... very UK "ting." Would love to chat one sunday in April and see if there are learnings - feel free to use Meet-Ting to help get something on the calendar :)
Crafted by Rajat
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