Bean On Bar September

Bean On Bar for iPhone and Apple Watch

You don’t brew a dripper.
You brew a bean.

A cone has no opinion. What decides your cup was decided long before the water hit it — at altitude, at the picking table, at first crack. Bean On Bar reads the bean first, and builds the recipe from that.

On the App Store in September.

A glass of filter coffee in hard side light
The cup at the end of it. Everything that decided how it tastes had already happened before this point.
Free with your next bag $29.99 once — no subscription Exportable always

01 — The product

From bag label to
repeatable cup.

Built around the coffee habit loop: identify the bean, understand the context, brew with guidance, log what happened, and make the next cup easier.

95Clearly documented
Provenance
Estate named · variety · process · roast date inside the window
Specificity
Tasting notes and weight, capped so a longer label cannot outscore a shorter one
Depth
Estate named · price visible — separate markers, never dragging a plain bag down
Taste signals
Review and competition data cited as badges, never folded into the score
Algorithm
Coach research-derived v1

Context Scores are automated reads of visible listing detail, not verdicts on quality.

Ground coffee beside a card naming the lot, process and tasting notes
Ethiopia, Alo Faficho. 74158, washed. Four fields and a note — no altitude, no roast date. A score can only read what is printed.

At the counter

This is the whole of it. A bag still in someone’s hand, a phone, and eight lines in view — no booth, no tripod, nobody asked to wait.

The reader says slow down before it offers to capture. A confident wrong read costs more than a second attempt.

A phone held over a coffee bag, its camera view reading the Corazon de Jesus, Costa Rica label, with the words slow down, eight lines in view, and a Capture button
Eight lines in view, and the phone saying slow down before it offers to capture. The label is read on the device; the bag stays where it is.
The Bean On Bar capture screen: New bag, At the counter, Browsing, with Scan the bag and Read 30 lines from the label
Thirty lines read off the label and shown back as a record — each field marked LABEL, including where the read went wrong. Nothing is saved until you have corrected it.
The Today screen: Colombia La Ciudad on day 26 and likely past peak, then pick up where you left off with an Apartment Coffee Ethiopia on a Hario V60, last cup five stars and balanced, grind dial 8.3, dose 15 g to 250 g
Dial 8.3, 15 g to 250 g, and a coach that says repeat it before changing anything — because the last cup was already balanced. Above it, the bean you are on and how far past peak it has drifted.

02 — The loop

Green, roast, pour, cup —
and back again.

Plenty of tools log a roast, and plenty log a brew. What is unusual here is that the file moves in both directions — your roaster’s .klog comes in, a green-adapted .kpro goes back out, and what the cupping table said becomes the next roast’s edit.

first crack

Roast

Bean temperature against rate of rise, read from your own .klog.

measuredintended

Brew

What the scale saw, against what you meant to pour.

day 9–14

Rest

The window this lot is likely to be at its best.

What a roaster chooses to disclose is the whole of what can honestly be scored. Everything the app says about a bag is a read of what is already printed on it.

And everything you do with it is timed on your wrist rather than on a phone propped against the kettle. The watch runs the whole brew — bloom, pours, drawdown — into the same record. Following along is free; starting the brew from the watch itself is part of Pro.

How the watch works

03 — Capabilities

Small details, decided
the honest way.

The native app is strongest when it stays close to the moment: read the bag, choose a starting recipe, run the timer, taste the cup, keep the record under your control.

Camera-first, editable by design

Capture or choose a bag photo and let Apple Vision read the label on-device. Every field the app suggests — roaster, bean, origin, variety, process, roast level and date, tasting notes, price, weight — stays yours to correct before anything is scored or saved.

Context you can inspect

Visible label detail becomes a Context Score with its reasoning shown. Cited reviews, competition recognition, and roaster-stated cues appear as separate Taste Signals, so the app never invents review authority it does not have.

Filter and espresso, tuned from the bean

Start from an unknown, identified, or saved bean. Choose your brewer, scale the dose, translate grind ranges to the grinder you actually own, and adjust by taste rather than by recipe card.

Personal benchmarks, not fake averages

Price per gram is compared against your own saved history in your own currency. No invented community average, no leaderboard, no number that needs other people to exist.

Travel that comes home with you

Plan a trip, shortlist cafes that survive going offline, log what you drank at the counter, and get told when a bean you loved abroad turns up on a shelf near you.

Portable by design

Saved beans, notes, recipes, and brew logs export as JSON, with optional user-controlled iCloud backup. The notebook stays account-light and movable, and we never hold it. The exception is a cup you deliberately share, which becomes a readable link.

No analytics in the app. No exceptions.

No analytics SDK, no advertising SDK, no tracking identifier, no account. Bean On Bar declares NSPrivacyTracking = false in its privacy manifest — a declaration Apple publishes but does not audit — and your notes, label text, photos, prices and locations never leave the device unless you share a cup on purpose.

A recipe is a record of what someone else’s water and someone else’s grinder once produced. The point was never to follow it exactly — it is to know which number to move when the cup in front of you is not theirs.

What the label is telling you