MicroYards — AI garden design and live price engine
Digital Bridge built the MicroYards core engine: a generative AI image pipeline that redesigns a small garden from one user photograph, then a live price engine that matches the generated plants, pots, lighting and furniture to real products across multiple retailers within a stated budget. Client: MicroYards. Sector: Consumer marketplace. Engineering class: Applied AI product engineering.
What problem was this solving?
Design inspiration and shopping are normally two disconnected experiences. A generated image is worthless to a consumer if the products in it cannot be bought at a price they can afford.
What did we build?
Generative redesign from a single photo, paired with a live multi-retailer price engine that keeps the result inside the user's budget.
- Photo-to-design generative pipeline producing a plausible redesign of the user's own space.
- Object extraction mapping generated elements to purchasable product categories.
- Live price comparison across Amazon and partner stores.
- Budget constraint solving so the returned basket fits the user's stated spend.
What does it integrate with?
The integration surface technical buyers ask about.
- Generative image provider invoked server-side.
- Retailer product feeds and affiliate endpoints.
- Budget solver sitting between the design output and the basket.
How does the system actually work?
The decision path in order. Client-confidential detail is deliberately excluded.
- 1. Upload — User submits one photograph of their existing space.
- 2. Generate — Server-side generative pipeline produces the redesign.
- 3. Extract — Design elements mapped to purchasable product categories.
- 4. Price — Live lookup across Amazon and partner retailers at request time.
- 5. Fit budget — Basket solved against the user's stated spend and returned.
What made it hard?
The real engineering constraints on this build.
- Generated designs must stay achievable — an image full of products nobody sells is a bad product, not a creative one.
- Retail prices move, so the basket is priced at request time rather than cached indefinitely.
- Image generation cost per user has to stay below the value of the resulting basket.
What was the outcome?
MicroYards ships a consumer product where the AI output is directly actionable rather than decorative. No third-party traffic or revenue figures are published here. No measured performance figures are published for this engagement because the client has not authorised their release.
What capability does this demonstrate?
Engagement band: Applied AI engineering band.
- Consumer-grade generative image pipeline
- Generated-object to real-product mapping
- Multi-retailer live pricing
- Budget-constrained recommendation
How do you stop the AI generating things that cannot be bought?
The design step and the commerce step are separated. Generated elements are mapped to real product categories, and anything that cannot be matched to a purchasable product is dropped from the basket rather than shown as if it were buyable.
Does the price update in real time?
Prices are looked up at request time across the connected retailers rather than served from a stale cache, because a wrong price is worse than a slow one.
Is this the same engine you would build for our business?
The pattern transfers — generate, map to a real catalogue, constrain by budget. The catalogue and constraints would be yours.
A note on what is published here
Case studies on this page describe work Digital Bridge delivered. Client-confidential detail, credentials and internal data structures are deliberately excluded. Where a client has not authorised publication of measured figures, outcomes are described qualitatively rather than quantified — we do not publish numbers we cannot evidence. Last reviewed 2026-08-18.