alt Hacker NewsLaunch HN: 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." 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!
Comments
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!
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.
> 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!
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.
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?
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.
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...
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 :)
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!
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 :)
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.
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.
[0]: https://claralabs.com