Roastiva Lab is an app that helps users manage the entire journey from bean bag to cup. The daily dashboard suggests which bags to use first and displays the last brew, ongoing experiments, caffeine intake, remaining quantity, and next steps. A knowledge library provides information on species, varieties, growing regions, processing methods, roast levels, flavor profiles, brewing methods, grind sizes, ratios, temperatures, timing, and storage. Bean inventory tracks roaster details, origin, roast date, opening date, remaining weight, storage conditions, recipes, and brewing history. Freshness simulation—based on time since roasting, opening date, storage method, remaining beans, and usage rate—determines which bags to prioritize. A testing lab allows adjustments to coffee quantity, water volume, ratio, grind size, temperature, time, bloom phase, number of pours, and agitation levels to predict strength, extraction, body, acidity, bitterness, sweetness, and clarity. Brewing guides provide timers, water targets, and pour-stage instructions, comparing the plan against actual execution. A brew log records the bean bag, recipe, parameters, yield, deviations, and results. A cup evaluation section captures aroma, acidity, sweetness, bitterness, body, clarity, aftertaste, balance, and preference level. A "cup rescue" function analyzes flaws—such as excessive acidity or bitterness, weak or strong brew, dry mouthfeel, weak aroma, or incorrect flow—and suggests adjustments for the next brew. Controlled experiments allow for changing a single variable to record whether results improve, remain unchanged, or worsen; each recipe adjustment creates a new version while preserving history. A taste profile is built from user-entered data to identify preferences regarding roast level, body, acidity, processing method, flavor notes, and brewing style. Brewing method and caffeine tools that estimate levels based on bean type, dosage, brewing technique, yield, and consumption (presented as ranges); a caffeine timeline tracking daily totals, remaining amounts, projected levels at bedtime, and the contribution of each drink; comparison features highlighting differences between bean types, recipes, versions, storage methods, or schedules; and a summary analysis covering top-rated beans, best recipes, popular methods, common errors, experimental ratio adjustments, consumption and remaining bean quantities, as well as caffeine trends and taste preference shifts—with all data linked across beans, brews, ratings, experiments, and caffeine metrics to allow users to trace the origin of every conclusion.