1、What inspired you to build this? I kept Googling things like “how much caffeine is in a Starbucks latte” or “low caffeine drinks at Dutch Bros” — and the answers were always messy, inconsistent, or buried in PDFs. There was no simple place to: compare drinks understand caffeine clearly or know how it actually affects your body So I built LiqLark to fix that. 2、What problem are you solving? Drink data is fragmented and hard to use. Official menus aren’t built for decision-making, and most sites don’t help you answer practical questions like: “What should I order if I want low caffeine?” “How much caffeine is too much for me?” “When will this wear off?” LiqLark turns that into: clean, structured data searchable lists (low caffeine, keto, etc.) and tools to understand caffeine in real life 3、How did your approach evolve? I started by building a simple drink database. But quickly realized: data alone isn’t useful — decisions are. So I shifted to: ranking drinks (not just listing them) building pages around user intent (low caffeine, high caffeine, etc.) adding tools like caffeine metabolism calculators Now it’s less of a directory, and more of a decision engine for drinks. In one line LiqLark helps you choose what to drink — based on caffeine, calories, and how your body actually responds.

LiqLark

Ask anything about your drinks

Ask anything about your drinks