I built this because I’ve seen how much time founders waste pitching the wrong investors. Evalyze reads a deck, builds a quick profile of the startup, and compares it to investor data like stage, sector, check size, and portfolio patterns. The result is a ranked list of investors with a short reason why they match.
Under the hood it’s a mix of LLM parsing and embeddings, with some rules layered in. The data is public and updated regularly. The stack is pretty simple: Next.js, FastAPI, Postgres.
It’s not perfect yet. Thin decks and biotech are tough, and some investor profiles get outdated. I’m working on ways to show uncertainty better.
Would love feedback on whether the matches feel useful, where you see bad fits, and how much explanation you’d want on the ranking. I’ll stick around to answer questions.
I built this because I’ve seen how much time founders waste pitching the wrong investors. Evalyze reads a deck, builds a quick profile of the startup, and compares it to investor data like stage, sector, check size, and portfolio patterns. The result is a ranked list of investors with a short reason why they match.
Under the hood it’s a mix of LLM parsing and embeddings, with some rules layered in. The data is public and updated regularly. The stack is pretty simple: Next.js, FastAPI, Postgres.
It’s not perfect yet. Thin decks and biotech are tough, and some investor profiles get outdated. I’m working on ways to show uncertainty better.
Would love feedback on whether the matches feel useful, where you see bad fits, and how much explanation you’d want on the ranking. I’ll stick around to answer questions.