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CASE STUDY — DTC Fashion Brand

AI Operating System for a DTC Brand

How I built 15+ AI systems from the inside — as the CEO running the company — to create a fully integrated AI operating layer across product creation, SEO, pricing, and operations.

AI SystemsEcommercePythonMulti-LLM

● THE RECEIPTS

+38%
Revenue per employee
-42%
Manual ops time
29
AI-powered modes built
564+
Products dynamically priced
313
Blog articles managed
22,000+
Lines of custom Python
01

The problem

The brand was a 10-person handmade fashion company doing millions in revenue, but every process was manual. Product creation took days. SEO was ad hoc. Pricing was gut-feel. The team was stretched thin, and hiring more people wasn't the answer.

02

The move

Instead of buying off-the-shelf tools, I built custom AI systems tailored to our exact workflows. A multi-LLM architecture chains Claude, Gemini, and custom models for cost-efficient results. The product creation pipeline generates everything from SKUs to SEO descriptions. Dynamic pricing adjusts 564+ products based on margin targets. Content automation manages 313 blog articles with programmatic SEO.

What we shipped.

06 / DELIVERED

  • Built end-to-end product creation pipeline: idea → SKU generation → description → SEO optimization → Shopify deployment → collection merchandising
  • Multi-LLM architecture routing tasks to the most cost-effective model (Claude for writing, Gemini for bulk ops)
  • Dynamic pricing engine factoring COGS, competitor data, and margin targets across 564+ products
  • SEO automation managing 313 articles with programmatic optimization and internal linking
  • Inventory intelligence integrating ShipBob data with sales velocity for smarter purchasing
  • Custom dashboards for real-time visibility into AI system performance

— THE TIME IS NOW

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