Key takeaways
- Kollegio AI gives students college counseling guidance for free: college matching, AI essay feedback, scholarship discovery and direct admission pathways.
- Three of Sprout's people were on Kollegio's engineering team: Usman as founding engineer, Waseem (now Sprout's tech lead) and Noor (who leads Sprout's frontend work).
- The hard part was reliability at scale: clean data models, careful AI usage to control cost and latency, and an architecture that grows without a rewrite.
- Kollegio is Stanford-backed, funded by Reach Capital, and now serves about 1 million students and families.
Kollegio AI was built by a small founding engineering team that included three of Sprout's people: Usman, Sprout's founder, as founding engineer; Waseem, now Sprout's tech lead; and Noor, who leads Sprout's frontend work. They built college matching, AI essay feedback and scholarship discovery on Next.js, Node.js, Python and PostgreSQL. Kollegio is Stanford-backed, Reach Capital-funded, and now trusted by about 1 million students and families.
Kollegio AI set out to do something simple to say and hard to build: give every student the guidance of a $10,000 college counselor, for free. Here is how the product came together, and what it took to keep it reliable as it grew.
The problem
Good college counseling is expensive, so the students who need it most usually go without. Kollegio wanted to close that gap with software: match students to the right colleges, give real feedback on their essays, surface the scholarships they qualify for, and map out admission pathways. The hard part was making all of that feel personal for one student at a time while running reliably for a very large and growing user base.
What we built
| Feature | What it does |
|---|---|
| College matching | Weighs a student's profile against thousands of schools |
| AI essay feedback | Gives specific notes on application essays, not generic praise |
| Scholarship discovery | Surfaces the awards a student actually qualifies for |
| Admission pathways | Shows a clear route in, including direct admission |
| College data pipeline | Keeps deadlines, requirements and essay prompts current |
The stack was deliberately standard: React and Next.js on the frontend, Node.js (Express) and Python (FastAPI) services, PostgreSQL with Prisma, and the OpenAI API for the AI features.
Each feature had to do real work for an individual student while staying fast and dependable for everyone else.
Keeping it stable at scale
The interesting engineering problem with Kollegio was not any single feature. It was keeping the whole product reliable as usage climbed. Three things mattered most:
- Fresh, clean data. College counseling is only as good as its data. The team built scrapers and cleaning scripts that pull application deadlines, requirements and essay prompts early in each admission season, merge sources, and publish them to the database, so students are never working from last year's rules.
- Careful use of AI. AI calls are slow and cost money on every request. The team kept them for the tasks that needed them, like essay feedback, and handled everything else with ordinary code, so cost and response times stayed sane as usage grew.
- A fast, dependable frontend. Cutting redundant API requests and tightening client-side state management improved load times by about 35%, and cross-browser and cross-device fixes reduced production defects.
Underneath all of it: clear data models, tests on the critical paths, and CI/CD pipelines that made releases routine instead of risky. This is the same discipline Sprout brings to scaling any MVP into a real product: build the foundation so that growth does not break it.
The outcome
Kollegio is Stanford-backed, funded by Reach Capital, and now trusted by about 1 million students and families, by its own count. For Sprout, it is the clearest proof of how we work: AI products that are genuinely useful to one user and still hold up when hundreds of thousands arrive. If you are building something in that shape, that is exactly what we do.