Roaster, origin, producer, process, variety, price, freshness, evidence, cafe availability, and brew outcomes connected clearly.
BEAN ON BAR INTELLIGENCE
Better coffee starts with
data people trust.
Bean On Bar keeps coffee information understandable: what the bag says, why the score moved, how a recipe was chosen, where a bean was found, and what drinkers can correct when something is unclear.
Country and city-aware “cool beans near you” with purchase links, cafe reports, and freshness notes to verify before you buy.
Roaster recipes, personal brew attempts, dose changes, grinder notes, and taste outcomes attached to each bean.
Transparent scoring that shows the label signals and cited evidence instead of pretending to be a fabricated review score.
SCORING METHODOLOGY
A score that
shows its work.
The methodology is intentionally explainable. The app should reward disclosed provenance, freshness, specificity, and trusted evidence while warning users when the label is vague.
EXAMPLE SCORE
Kenya Kiambu AA
88Strong buy
This is not a review claim. It is a visible-signal score based on what the bag and cited sources disclose.
Origin country
Basic traceability starts with country-level disclosure.
Region named
Specific growing regions make comparison and discovery more useful.
Producer or farm
Named farms, producers, washing stations, or co-ops signal stronger provenance.
Process disclosed
Washed, natural, honey, anaerobic, and other process details affect buying and brewing.
Evidence support
SCA-style scores, Coffee Review, Cup of Excellence, WCR context, and pasted evidence can boost confidence when cited.
Missing roast date
Freshness matters. Missing roast dates should reduce buyer confidence.
BEAN INTELLIGENCE DIRECTORY
From scanned bag to
clearer decisions.
These prototype records reuse the community bean dataset. As people scan, brew, report, and correct beans, the directory can become a clearer guide to what is worth trying.
PRICE BENCHMARKS
Make value visible,
not mysterious.
Price context should help drinkers compare bag sizes and currencies without pretending every expensive coffee is automatically better or every cheaper coffee is a bargain.
Prototype estimates use sample prices for demonstration. In the app, price-per-gram comparisons should stay grounded in real labels and the user’s own saved history.
COMMUNITY SIGNALS
Every participant improves
the next coffee decision.
Coffee drinkers, travelers, cafes, and roasters each add a different kind of signal: what was scanned, brewed, served, sold, and enjoyed.
Drinkers scan and brew
Each scan improves bean coverage. Each saved brew adds taste outcome data that generic search engines do not have.
Travelers report availability
City-level reports answer the high-intent question: “What is worth buying near me today?”
Cafes claim what is on bar
Cafe profiles can become lightweight inventory pages for guest beans, retail shelves, and brew methods.
Roasters submit recipes
Official recipes travel with the bean, improving first-cup success and strengthening roaster visibility.
COMMUNITY HEALTH
Signals that show
people are helped.
As the community grows, the healthiest signals are simple: people identify beans more confidently, brew better first cups, correct unclear data, and rediscover coffees they loved.
Shows whether users repeatedly use the core wedge.
Shows whether Bean On Bar becomes a coffee memory, not just a one-time scanner.
Shows brew guidance is useful at the moment of making coffee.
Shows the local discovery graph is being built by users.
Shows roasters see value in attaching official guidance.
Lets drinkers, cafes, and roasters flag outdated availability, unclear recipes, or label details that need fixing.