TasteMatch Beer Curator

For

Craft beer enthusiasts at subscription box services

Solves

Subscription boxes send popular picks that ignore individual taste, leaving drinkers with bottles they will not finish.

Delivers

Personalised beer selection and taste profile

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For craft beer enthusiasts at subscription box services, turn personal taste notes, beer ratings and label images into a personalised beer selection and taste profile.

1. Capture taste preferences in free text.
2. Match beers to profile using sensory models.
3. Rate beers with text or photo.
4. Update profile after each rating.
5. Avoid repeat beers unless requested.
6. Generate a monthly box recommendation.

What goes in, what comes out

The workflow

1. In
   Start with

   Personal taste notes, beer ratings, label photos

2. 1

   Complete a taste quiz

3. 2

   Review initial beer matches

4. 3

   Receive a box

5. 4

   Rate each beer

6. 5

   Let the model update the profile

7. 6

   Preview the next box

8. 7

   Confirm the order

9. Out
   Finish with

   Personalised beer selection and taste profile

AI does the heavy lifting, people stay in charge

Use language models to interpret taste descriptions and ratings, and vision models to read label images. Match beers to profiles using a fine-tuned recommendation model. A human curator checks the final box selection for quality and availability before dispatch.

What your team sees

Key screens: Taste profile, beer library, box preview. Use a profile dashboard for preferences, a searchable beer library with filters, and a box preview showing the four selected beers with predicted match scores. Let users rate each beer with words or a photo. Display a learning progress indicator. Provide a reorder button for favourites. In this product, the first view is taste profile, followed by beer library and box preview.

Accounts and administration

User accounts, taste profile versions, box order history, rating logs, preference changes, admin review queue, and an audit trail of model updates.

Integrations and data access

Connect to brewery inventory systems, payment gateways, and shipping carriers, starting with a simple CSV feed.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will craft beer enthusiasts at subscription box services use it to solve "subscription boxes send popular picks that ignore individual taste, leaving drinkers with bottles they will not finish"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. A paid pilot with 20 subscribers proves it by measuring box satisfaction scores against a baseline of random picks; the decision is to expand if satisfaction rises by 30 percent.
4. Measure, then decide. Track two measurable outcomes: average rating per box and repeat subscription rate after three months. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. First cut: craft beer subscribers, one use case for monthly boxes, modules for taste quiz and box curation, manual review of every box by a human curator.

After the MVP. Automate the curation review after enough ratings, add voice ordering, and integrate brewery inventory feeds for real-time availability.

What the build depends on. The build needs a fine-tuned language model for taste matching, a vision model for label reading, and a structured beer database with sensory tags.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at USD 39 per box, with a USD 59 tier for rare or aged beers, as a hypothesis.

Message to test

Never settle for a random six-pack again, get a box that learns your taste.

Where to find buyers

Craft beer forums, subscription box review sites, and social media groups for beer enthusiasts.

Lead magnet

Offer a free taste profile report with three predicted beer matches.

The first 30 days

1. Week 1: Build the taste quiz and profile storage.
2. Week 2: Integrate a beer database and basic matching model.
3. Week 3: Create the box preview and rating interface.
4. Week 4: Run a manual curation pilot with ten users.

Paid pilot

A paid pilot with 20 subscribers proves it by measuring box satisfaction scores against a baseline of random picks; the decision is to expand if satisfaction rises by 30 percent.

Success metrics

Two measurable outcomes: average rating per box and repeat subscription rate after three months.

Retention and expansion

It keeps earning through monthly subscription fees and higher tiers for rare beers, with the profile improving each month to reduce churn.

Why clients would pick it

The model improves with each rating, creating a data moat of personal preferences that competitors cannot copy without years of user behaviour.

Alternatives and positioning

People use generic subscription boxes or browse brewery websites manually; this differs by offering a continuously learning personal curator.

Main delivery costs

Main delivery costs are beer procurement, shipping, model inference, and human curator review time.

Limit profile edits to the owner, require human approval for any box before dispatch, and do not recommend beers outside the user's stated dietary or alcohol limits.