Loomlr

01 · The brief

An AI studio that had to feel like a studio.

Loomlr turns real product photographs into on-model fashion imagery. Brands upload the garments they already have, build reusable digital-twin models, poses and backgrounds, preview complete outfits and publish approved shots back to Shopify.

The category is crowded with text-to-image tools that promise everything and drift on every batch. Fashion teams have been burned by faces that change, garments that mutate and backgrounds that reshuffle. Loomlr had to look and read like a serious production tool for people who care about how clothes photograph.

02 · What the page had to do

Prove consistency before anyone signs up.

The site had to win over ecommerce managers, creative leads and agencies who already know exactly what a photoshoot costs. It needed to:

• Position Loomlr against both traditional shoots and generic AI tools, and make the difference visible.
• Explain a real production workflow, from product upload to publishing, in a handful of steps.
• Answer the question every fashion team asks first: will the product still look like the actual garment?
• Show that it fits the way teams already work, with review, approvals and Shopify.
• Make pricing and return on investment easy to reason about.

03 · How we delivered

An editorial issue, not a SaaS template.

We designed the site as a fashion magazine: “Volume 01 · The Production Issue”. A fixed chapter index runs down the side, fifteen numbered sections from cover and manifesto to method, studio, process, numbers, review, integrations, pricing and Q&A, so a long page feels like a publication you leaf through rather than a scroll you endure. A high-contrast serif for display, a clean sans-serif for reading and real model photography do the rest.

Every chapter answers an objection. The method section sets the traditional booking cycle against Loomlr's reusable system, point by point. A studio tour steps through the six tools: products, outfits, models, backgrounds, the composer and AI edits. A side-by-side shows generic AI drifting on the left and a locked model identity on the right, and a twelve-point capability matrix spells out what text-to-image generators can't do.

Production reality gets its own space: external review links with shot-level comments and approvals, export presets for ecommerce channels, generation queue controls and credit visibility. Benchmarks are presented as planning ranges with a published methodology, and pricing sits next to a credit calculator, a buyer guide and an ROI calculator.

The Q&A closes with the nine questions teams ask before trusting AI with their catalogue, starting with whether the garment will still look like the garment.

04 · Built to be found

Comparisons and resources that rank.

Around the homepage sits a resource library of guides and calculators, plus dedicated comparison pages against competing tools and generic AI image generators, the pages buyers read when they are choosing. Organization, SoftwareApplication, WebPage and FAQPage structured data describe the product to search engines, and an llms.txt file does the same for AI assistants.

The site is built with Next.js and prerendered for speed.

05 · What shipped

The deliverables.

• Editorial concept and art direction for the site
• Landing-page copy and chapter structure
• Interactive studio tour and side-by-side comparisons
• Pricing, credit calculator and ROI tools
• Resource library and competitor comparison pages
• Structured data, sitemap and llms.txt
• Next.js build, deployed on Vercel