Deep dive
Planning, not scrolling
Visual discovery apps succeed when they help people make decisions. Someone redesigning a kitchen saves dozens of images, compares them, shares a board with a partner and eventually buys tiles and a tap. The product should support that journey: easy saving, boards that organise ideas, search that refines what you mean and pins that lead to a source, a recipe or a product.
Pick a vertical where that journey is valuable and the content is plentiful. Interiors, weddings, fashion, recipes, gardening and crafts all fit. A vertical also decides your merchant partners, your moderation rules and your first creators. Our MVP development process keeps the first release focused on saving, organising and finding.
Seed the catalogue before launch. Import high-quality images from partner creators and brands with permission, build starter boards for common projects and let new users pick interests during onboarding so their first feed is useful. An empty or generic first feed is the most common reason people never return to a discovery app, and it costs far less to fix with curation than with algorithms.
How related pins and visual search work
Each image is passed through an embedding model that turns it into a vector capturing its visual content. Similar images have nearby vectors, so related pins are a nearest-neighbour search in a vector index. Combine that with text signals, board co-occurrence, which is when people save two pins to the same board, and engagement, and you get recommendations that feel curated.
Visual search extends the same idea: the user selects a lamp in a photo, the app detects the object, embeds the crop and finds similar pins or products. Pretrained multimodal models make this affordable to start; tuning on your own data improves it. Read our guide to choosing a vector database for the storage choice.
- Embed images once at upload and store the vector alongside the pin.
- Deduplicate near-identical images so the feed does not repeat itself.
- Blend visual similarity with text and engagement signals rather than relying on one.
A grid that stays fast
The masonry grid is the product's signature and its main performance risk. Request images at the exact width of a column, use AVIF or WebP where supported, show dominant-colour placeholders while images load, and virtualise the list so off-screen images release memory. Test on mid-range Android phones and slow networks, where grids first become janky.
On the web, the same pages are your search engine presence. Server-render pin and board pages, give each a unique title and description, add structured data and keep Core Web Vitals healthy. Thin or duplicate pages hurt rankings, so consolidate near-duplicates and noindex empty boards.
Commerce without losing trust
Product pins turn inspiration into revenue. Merchants supply catalogue feeds with images, prices and stock; the platform keeps them current, labels shoppable pins and tracks clicks and conversions. Affiliate links and promoted pins follow. Keep ads clearly labelled and relevant, because a feed that feels like a catalogue loses the planners who make the product valuable.
Commerce also adds obligations: accurate prices, consumer protection rules, and privacy rules for conversion tracking under the GDPR and India's DPDP Act. Our ecommerce development team handles catalogue integrations and checkout.
Moderation and running costs
Image platforms need screening for nudity, violence and self-harm content, hash matching for known illegal material, spam link detection, copyright takedowns and reporting. Platforms used by teenagers also need safer defaults under rules such as the UK Online Safety Act and the amended US COPPA rule. AI labelling duties, such as India's 2026 rules on synthetic media, apply when users share generated images.
Running costs centre on image storage and delivery, embedding computation, the vector index and search. Maintenance is roughly 15-20% of the build cost per year. Because most traffic is reading, edge caching keeps costs manageable as the catalogue grows.