Knomor

Hey everyone — Ryan here. Thanks for checking out Knomor. Why I built this I’ve always felt like personal finance apps don’t actually understand my spending. They rely on shallow, inherited categories (“Shopping,” “Other,” etc.) that tell you nothing about what really happened. When I looked at my own transactions, I kept thinking: “There’s so much information hiding in each of these. Why isn’t anyone extracting it?” I wanted a tool that didn’t just display transactions — but studied them. The problem Banks and most finance apps treat transactions like generic line items. They copy the same limited metadata and move on. There’s no context, no meaning, no structure. As a user, you’re left guessing: - Was this lunch or groceries? - Was that Target run household stuff or a gift? - Is this spending pattern actually trending, or is it noise? The world runs on transactions, yet the analysis behind them is surprisingly shallow. What Knomor does differently Knomor examines every single transaction individually. Not just category-level guesses — real analysis. We pull out richer context, patterns, hints, signals, and structure. Enough that in theory, you could write a whole page about a single transaction. This lets you finally see where your money goes with clarity that feels almost unfair. How the approach evolved When I started, Knomor was “just” a consumer budgeting tool. Then I realized the real magic wasn’t the budgeting UI — it was the transaction intelligence underneath. The deeper I built the labeling engine, the more I realized: personal finance is only the first expression of a much bigger capability. That pushed me to build infrastructure that combines curated data, multi-layer analysis, pattern extraction, and a constantly improving labeling system — all designed to understand spending at a level no consumer app has tried before. Where this goes long-term This launch is the starting point. Today, Knomor is a personal finance product you can use. But the underlying tech is meant for much more: universal transaction categorization, merchant intelligence, trend and pattern detection, a research-grade financial dataset, and eventually ROP (Receipts Over Payments) — attaching rich digital receipts at the payment-rail level. The long-term goal is to build the transaction intelligence layer that doesn’t exist yet, serving both everyday people and future developers. For now I’d love for you to try the app, poke around, label things, and tell me where it breaks or where it surprises you. I poured eight months into Knomor, and this is just Day One. Your feedback here will genuinely shape what comes next. Happy to answer anything about the product, the tech under the hood, or where this is headed. — Ryan