Hi HN, I’m Danial, co-founder and CTO of Risely AI (https://risely.ai). We're building AI agents that automate operational workflows inside universities. Here’s a demo: https://www.loom.com/share/d7a14400434144c490249d665a0d0499?....

Higher ed is full of inefficiencies. Every department runs on outdated systems that don’t talk to each other. Today, advising staff are looking up enrollment data in PeopleSoft or Ellucian, checking grades and assignments in Canvas, and trying to track engagement in a CRM, if they even have one. Often, it’s just spreadsheets and email. One advisor told us they were losing 8+ hours/week just trying to answer: “Which students are struggling?”. During that lag, students slip through the cracks, and every lost student costs a school tuition.

I’ve spent the last decade building large-scale systems, but about a year ago, I left my job to build something personal. My time at UC Berkeley reinforced what my parents taught me when we immigrated to the U.S. - that education is the most powerful tool for upward mobility. But nearly 40% of students never graduate. Many of these students are capable and just need support, but the systems meant to support them are overwhelmed and broken.

So we built Risely. Our first agent focuses on academic advising and retention. It connects to a school’s systems, unifies the data, flags at-risk students, drafts outreach, and answers natural-language questions about caseloads and course progress. It gives staff leverage and time back, while helping more students stay on track.

The harder part is everything under the hood: - Connecting to archaic SIS, LMS, and CRM systems with inconsistent APIs and data models - Normalizing messy institutional data into something agents can reason over - Handling real policy constraints around FERPA, isolating tenant data, and meeting strict security and privacy standards for student PII - Designing agent workflows that are traceable, reviewable, and safe to run in production - Building infrastructure that can adapt to different institutional rules, processes, and edge cases.

We started with advising because retention ties directly to both revenue and student success. But the same foundation applies to registrar, admissions, financial aid, research administration, and other critical functions. As more agents come online, they can begin to coordinate with each other and hopefully improve the entire operations of a college or university.

If you’ve built systems that had to reconcile messy data, inconsistent workflows, or policy constraints using LLMs, we’d love to hear how you approached it.

We’d love to hear your thoughts about the above, and anything in this space!

All of those system of records are adding exactly those capabilities and bundling them at no extra cost. How do you plan on overcoming that?

We should talk. I used to work with universities.

The key issue for the sector is the tens of legacy systems that don't integrate with each other, often with manual spreadsheet processes that could be easily automated. Yet the big players like Oracle sell a generic CRM experience that doesn't fit well with higher education.

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.

How's it like working with schools/universities as a startup? I've always heard edtech can be a slow, bureaucratic sales cycle (and maybe not a high willingness to pay?).

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?

Impressive and a good mission startup! How did you get workday, peoplesoft etc. to give the data to you? I assume all these companies do not like to share data, since as someone else also pointed out that each of these system of records are adding AI capabilities and bundling them.

The recommendations don't look very insightful, and seem like a rephrasing/summary of the alert above it. For example the first student who has account holds, bad grades, etc. the recommendation is just to schedule a meeting. I don't think the LLM will be able to provide super insightful recommendations. Even in the improvement plan generated by the agent, the steps seemed pretty generic(as expected from LLMs).

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.

Very cool work. I particularly like your focus on student outcomes and building a curriculum that extends beyond the classroom to extracurriculars. I hope you succeeed.

Sounds like a massive band aid. What do you do when that band aid is no longer maintained properly? You wouldn't be able to rebuild the systems underneath because the staff are now reliant on interfacing with the band aid.

Wouldn't it have been a better long term fix to replace the antiquated systems?

We should chat. We rapidly capture the existing operating models in universities for analysis and optimization of administrative workflows.

I'm 10+ years into IT in higher ed. I'm intrigued by the ideas here. Do you envision Risely being entirely a reporting system that runs _against_ existing systems and data, or do you envision Risely being another source of truth where some data lives? Because if it's the latter, I'm feeling big xkcd.com/927 feelings.

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.