Fashion & Luxury Ecommerce
AI for fashion ecommerce, built on the client book.
Fashion ecommerce margin lives in the client book — and leaks out through the returns queue. We build the AI agents that work both: your clienteling rules, sizing conventions, and outreach tone, encoded inside the data and email stack you already run.
Built with the leading AI platforms
The pain map
Fashion ecommerce margin leaks one return — and one lapsed client — at a time.
From client-experience VPs, styling-team leads, and data leads inside a luxury fashion e-commerce retailer — designer apparel, trunk shows, personal-styling tiers, ~12.5K client profiles under management, and a Snowflake-and-Looker data spine with Klaviyo carrying the sends.
Returns are the board’s number one opportunity
“Mitigating returns is our number one opportunity.” The follow-up window is 48 hours, and the outreach should offer the next size down or a similar piece in the client’s palette — not re-show the item she sent back. Half of it is sizing: UK 10, French 34, Italian 38, and designers who don’t conform to standard size-scale conventions, so her most consistently purchased and kept size has to be computed per designer.
Top clients drift before anyone notices
High value drift: a top-spending client starts going quiet — not lost yet, but drifting. Stylists carrying ~350-client books spend a disproportionate part of their time in a very small group and miss the rest; a concierge rep carrying ~2,000 can’t see it at all. And the $5K tier line misses people — there are clients below the threshold who might be billionaires, and a household name can buy on the site without anyone being told.
Outreach that can’t read like AI wrote it
“Make sure this doesn’t sound like I’m speaking to someone I don’t know” — it should not look like it’s written by AI. And if the queue raises the cognitive load — what is the action, is this accurate — stylists will just close it and do what they usually do. The pilot can’t be a feedback loop that goes on forever; land the plane.
Enrichment costs grow with the client book
Per-profile charges grow with the size of the client book and how often profiles are refreshed. Compare model quality and total run cost for private and API-based enrichment before choosing a deployment.
A data spine held together by hand
The warehouse is kind of chaos — the orchestration layer got built by hand. The dashboards carry site-credit values 24 hours late, and the email platform alone carries 617 distinct sends across 584 scheduled campaigns — which is how clients get emailed about pieces that aren’t available.
Where AI pays
Where an AI agent for ecommerce pays first.
AI in fashion ecommerce doesn’t pay on the homepage — it pays in the book. Each card is a workflow or capability built on the same pattern: your clienteling rules, sizing conventions, and outreach tone, encoded inside the systems you already run.
Questions
Asked by client experience and styling leads.
The straight answers, before you book anything.
What’s holding your business back?
A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.
Talk to Zaigo

