Launch HN: Risely (YC S25) – AI Agents for Universities
Are you hiring? I have 8 years of university SIS implementation & migration experience and 2 years of Edtech AI engineering experience and this is the exact problem space I want to work in.
Would love to chat! Feel free to reach us at hiring@risely.ai
I do think you have value in pulling in the disparate data sources and using LLMs to present the data in a clean way to the advisor/user.
We've found the "chat" functionality to be especially useful for advisors since we've been able to surface insights to them without them having to log onto many different systems and just present it in a clean output, as you pointed out.
I know a few different companies who ultimately moved out of the education market completely or just try to leverage their education traction as a beachhead to other markets. It sounds like you're focused on the education market - what's your take?
Staff and administrators are also just people working in critical functions. When the tools help with their day-to-day job functions, the willingness to adopt is there. We’ve stayed focused on education because the problems are tied directly to retention and student success, and those are outcomes schools care deeply about.
We should talk. I used to work with universities.
- Integration tax: Each module still lives in its own data model. Schools end up exporting CSVs or building Mule pipelines to reconcile SIS+LMS+CRM. Our agent sits on top of all sources with pre-built connectors and a unified schema, so coaches see enrollment + Canvas grades + attendance in one call (like in the Triage Center)
- Operational burden: Products like Data Cloud or Agentforce are powerful but need admin capacity that smaller schools just don’t have. We ship a default ruleset for advisors + prompt library so an advisor can be productive immediately.
- Cost creep: Several platforms meter GPT usage or require new AI licenses. We price per active student so budgeting is predictable, which is a big plus for universities and their unique budget cycles.
Curious if you’ve found pain points around data normalization especially (this is the hard, very custom part of our work right now). Happy to keep the discussion here for the benefit of others, and if you’d like to dive deeper my email is sadia@risely.ai
We're a small non-profit liberal arts school, and we already have 70+ integrations feeding to and from the various sources of truth and systems of record. It's a mess.
We intend to be an interoperable layer that sits on top of these systems, and allows users to not only surface valuable insights but also take actions within those systems in a secure and compliant way. You can think of it less as reporting and more as a “system of work” that leverages LLMs and agents to streamline the messy, cross-system tasks that slow people down today.
Where those systems are more closed, we work with the institution to find creative but still sanctioned paths such as through their integration hub or data warehouse. That way we are not asking the vendor for special access, just making better use of the plumbing that is already there.
Wouldn't it have been a better long term fix to replace the antiquated systems?