Taste Matching
Translate everyday flavor preferences into practical coffee recommendations.

Problem
Buying coffee is surprisingly hard. Flavor language varies by roaster, recommendations are often generic, and people end up choosing beans with very little confidence.
People want better coffee, but most recommendation experiences assume they already know the vocabulary. BrewMatch exists for the moment before expertise, when someone knows what they enjoy but not how to translate that into a good bag of beans.
Solution
BrewMatch turns personal taste, brewing habits, and caffeine preferences into a short, useful set of recommendations. It explains choices in plain language so discovery feels guided, not technical.
Home brewers, gift buyers, and curious coffee drinkers who want practical guidance without becoming experts first.
Key features
Translate everyday flavor preferences into practical coffee recommendations.
Tune suggestions around pour-over, espresso, French press, or daily drip routines.
Respect strength, roast, and energy preferences without making the choice clinical.
Surface beans that fit the drinker instead of overwhelming them with inventory.
Offer small guidance moments that make the first cup more likely to succeed.
Visual gallery
Product views are presented as quiet exhibits: frameless, readable, and built to reward closer inspection.
Technology
Fast product surface with static routes and responsive rendering.
Typed product logic for recommendations and preference modeling.
Taste signals are mapped to useful coffee attributes.
Built for quick discovery on phone or desktop.
Current status
The product is moving toward richer roaster inventory integrations and clearer side-by-side comparison views.
Next step