Personal Jewellery Stylist

For

Women in their 30s and 40s who buy jewellery monthly

Solves

Subscription jewellery services ignore individual taste and force mass-produced pieces.

Delivers

One jewellery piece monthly, taste profile, design briefs, wear report

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $18,000 for the full product

Run it

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

For women in their 30s and 40s who buy jewellery monthly, turn photos of existing collections, Pinterest boards and voice notes into one commissioned or curated piece shipped monthly.

1. Extract taste signals from photos and voice notes.
2. Build and update a taste map.
3. Match profile to workshop inventory.
4. Generate design briefs for commissions.
5. Track wear and return signals.
6. Manage shipping and quality checks.

What goes in, what comes out

The workflow

1. In
   Start with

   Photos of existing jewellery, Pinterest boards, voice notes, wear feedback

2. 1

   Share initial signals

3. 2

   Receive taste profile

4. 3

   Review monthly match

5. 4

   Approve or return piece

6. 5

   Send feedback

7. 6

   Receive next month's piece

8. 7

   Track wear history

9. Out
   Finish with

   One jewellery piece monthly, taste profile, design briefs, wear report

AI does the heavy lifting, people stay in charge

Use vision models to read jewellery style from photos and voice-to-text to convert voice notes into structured briefs. Match profiles to workshop inventory using structured attributes. A human stylist reviews each match and a quality checker verifies the piece before shipping.

What your team sees

Key screens: Taste profile, monthly match, piece detail. Use a card-based dashboard for profile signals, a large central panel for the monthly match, and a right-hand panel for piece details, materials and occasion. Let users approve or return with one tap. Display wear history and preference evolution. Provide a chat interface for voice notes and feedback. In this product, the first view is taste profile, followed by monthly match and piece detail.

Accounts and administration

User accounts, taste profiles, order history, return records, workshop contracts, quality check logs, shipping status and audit trail for all matches.

Integrations and data access

Pinterest API, Instagram API, workshop inventory systems, shipping carriers, payment gateway.

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 women in their 30s and 40s who buy jewellery monthly use it to solve "subscription jewellery services ignore individual taste and force mass-produced pieces"?
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. Run a paid pilot with 20 women for three months.
4. Measure, then decide. Track return rate below 10% and wear rate above 80%. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Start with 20 women in one city, one workshop network, taste profile module and monthly match module. Manual review of each match by a stylist.

After the MVP. Automate taste extraction, workshop matching, design brief generation and quality checks. Add more workshops and regions.

What the build depends on. Vision models for jewellery recognition, voice-to-text for briefs, workshop network with API or manual onboarding.

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 a monthly subscription of $120 with a $40 margin on each piece.

Message to test

One piece, made for you, every month.

Where to find buyers

Instagram, Pinterest, fashion blogs, jewellery forums, word of mouth.

Lead magnet

A free taste profile report based on three photos and one voice note.

The first 30 days

1. Week 1: Recruit 20 pilot users and collect initial signals.
2. Week 2: Build taste profile module and source five pieces from local workshops.
3. Week 3: Ship first pieces and collect wear feedback.
4. Week 4: Refine matching algorithm and prepare second month.

Paid pilot

Run a paid pilot with 20 women for three months. Baseline is current return rate of 30% for subscription boxes. Success is return rate below 10% and 80% wear rate. Decision: expand if metrics are met.

Success metrics

Return rate below 10% and wear rate above 80%.

Retention and expansion

Monthly subscription fee and improved taste profile that increases satisfaction and reduces churn.

Why clients would pick it

The taste data becomes a moat over time. Each delivery and return signal improves the profile, making it harder for competitors to match the personalisation.

Alternatives and positioning

Static style quizzes and mass-produced subscription boxes. This differs by using continuous visual and voice signals and commissioning unique pieces.

Main delivery costs

Workshop commissions, shipping, quality checks, AI infrastructure, stylist review time.

Limit data collection to explicit user consent. Do not share personal style data with third parties. Ensure all pieces meet quality standards before shipping.