Building Financial Data Monitor
Visit →Background
I ran a $1.3M student-managed investment fund during high school. Finding usable deal flow was harder than it should have been. Announcements were scattered, duplicated, delayed, or written for investors operating at a different scale.
Financial Data Monitor is a system for tracking early-stage funding signals. The goal is a cleaner feed of company, stage, sector, source, amount, and announcement timing.
Architecture
The pipeline pulls from structured and semi-structured sources, normalizes records, and deduplicates companies that appear across multiple announcements. The interesting work is not scraping. It is deciding when two imperfect records describe the same event.
The matching layer compares company name, round size, source, stage, and announcement window. Ambiguity is preserved when confidence is low, because false certainty is worse than an incomplete feed.
Product
The interface is a filterable funding feed. Users should be able to answer a few questions quickly: what changed, who raised, how much, from whom, in which market, and whether the signal is worth following.
I cut the original feature list before launching. The useful product was not a full investor CRM. It was a focused monitoring surface with credible source links and clean filtering.
What I learned
Data products fail quietly. A dashboard can look organized while the underlying records are duplicated, stale, or overconfident.
The lesson was to treat confidence as part of the product. Source, timestamp, normalization, and deduplication quality matter as much as the card on the screen.