Fit-First Wardrobe Service

For

Style-conscious professionals who buy clothes 4-6 times a year and hate returns

Solves

Clothing subscriptions still send boxes where half the items don't fit.

Delivers

A box of 5 made-on-demand garments adjusted to the member's body, with fit confidence scores

Built in

about 3 weeks of creation time, MVP in 4 days

Investment

$7,500 for the MVP, $27,000 for the full product

Run it

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

For style-conscious professionals who buy clothes 4-6 times a year and hate returns, turn three smartphone photos, style preferences and occasion needs into a box of made-on-demand garments adjusted to their body.

1. Extract 40+ body measurements from three photos.
2. Match measurements against brand size charts and fabric stretch data.
3. Interview style and occasion needs via voice or text.
4. Generate a coherent look with adjusted digital patterns.
5. Send digital files to manufacturing partners.
6. Update preferences from return reasons.

What goes in, what comes out

The workflow

1. In
   Start with

   Three smartphone photos, style preferences, occasion needs and return feedback

2. 1

   Upload photos

3. 2

   Complete style interview

4. 3

   Review fit predictions

5. 4

   Adjust preferences

6. 5

   Approve order

7. 6

   Send patterns to manufacturer

8. 7

   Receive box with 5 items

9. Out
   Finish with

   A box of 5 made-on-demand garments adjusted to the member's body, with fit confidence scores

AI does the heavy lifting, people stay in charge

Use computer vision models to extract body measurements and language models to interpret style preferences. Predict fit per garment using size charts, reviews and fabric data. A human stylist reviews the AI picks and a fit expert checks pattern adjustments before shipping.

What your team sees

Key screens: Body map capture, style interview, fit prediction dashboard, order review. Use a guided photo upload flow with live feedback, a chat-style preference capture, a grid of predicted fit scores per garment, and a final order summary with pattern adjustments. Let users see fit confidence per item and reason codes. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant garment. In this product, the first view is body map capture, followed by style interview, fit prediction dashboard and order review.

Accounts and administration

Member profiles, body map versions, order history, return reasons, stylist approvals, manufacturer records, pattern versions and an audit trail of fit predictions.

Integrations and data access

Manufacturing partner APIs, size chart databases, fabric data sources, payment processing and customer relationship management systems.

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

   4 days

   One buyer segment, one recurring use case; first modules: photo-based body mapping and fit prediction with human stylist review. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   8 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 style-conscious professionals who buy clothes 4-6 times a year and hate returns use it to solve "clothing subscriptions still send boxes where half the items don't fit"?
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 10 members proves the fit accuracy by comparing return rates against a baseline of 50% from standard subscriptions.
4. Measure, then decide. Track return rate per box and average fit confidence score per garment. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer: style-conscious professionals. One use case: 5-item monthly box. First two modules: photo-based body mapping and fit prediction with human stylist review. Manual review of all AI picks before shipping.

After the MVP. Automate pattern adjustment for common body shapes, integrate more manufacturing partners, add fabric stretch prediction, and use return data to retrain fit models weekly.

What the build depends on. Accurate vision models for body measurement, reliable manufacturing partner APIs, and a pattern adjustment engine that speaks to different factory machines.

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 49 per month subscription, plus USD 89 per kept garment, with a 5-item box.

Message to test

Get clothes that fit before they ship, made for your body from three photos.

Where to find buyers

Social media ads targeting style-conscious professionals, fashion blogs, and partnerships with personal stylists.

Lead magnet

A free fit prediction report showing how current brands' sizes would fit your body.

The first 30 days

1. Week 1: Build photo upload and body mapping prototype.
2. Week 2: Develop fit prediction model with existing brand data.
3. Week 3: Integrate one manufacturing partner and stylist review workflow.
4. Week 4: Launch pilot with 10 members and collect fit feedback.

Paid pilot

A paid pilot with 10 members proves the fit accuracy by comparing return rates against a baseline of 50% from standard subscriptions. Decision: proceed if return rate drops below 20%.

Success metrics

Return rate per box and average fit confidence score per garment.

Retention and expansion

Monthly subscription renews with a new box, and the AI improves fit over time, reducing returns and increasing kept-item revenue.

Why clients would pick it

The fit model improves with each return and body shape, creating a proprietary dataset of fit outcomes that competitors cannot replicate without similar volume.

Alternatives and positioning

People today use standard sizing, size charts and trial-and-error returns. This differs by predicting fit before shipping and adjusting patterns to the individual body.

Main delivery costs

Manufacturing partner fees per piece, AI inference for vision and fit models, stylist review time, and return logistics for failed fits.

Limit photo uploads to authorised members, restrict pattern adjustments to approved manufacturers, require stylist approval before shipping, and never share body measurements without consent. It must not ship garments without a human fit check.