Ecommerce ops · Production

Faire advances retail search, discovery, and developer productivity with AI agents

The problem

Retailers struggled to discover differentiated products without guessing keywords, wholesale depended on manual workflows across multiple tools, and engineers spent time on repetitive coding tasks that diverted focus from harder problems.

Workflow diagram · grounded in source
1
Retailer natural-language query
trigger
“buyers should be able to ask for what they want on our platform, similar to how they would speak to a rep: "Find me dresses made in Paris, under $100, and not sold on Amazon."”
2
Parse query to structured filters
ai_action
“Our system parses the phrase into structured filters under the hood and returns relevant products without the filter‑guessing game”
3
Return relevant products
output
“returns relevant products without the filter‑guessing game”
4
Image upload for visual discovery
trigger
“Upload an image and see visually similar products on Faire, turning a vibe into a shoppable result, especially on mobile”
5
Background coding agent execution
ai_action
“background coding agents take on well-scoped tasks (creating pull requests, writing tests, refactoring), so engineers can focus on harder problems”
6
Orchestrator dispatches sub-agents
ai_action
“a lightweight orchestrator that breaks work into reliable prompts, and a library of focused sub‑agents (e.g., settings cleanup, test authoring)”
7
Agent composes purchasable assortment
ai_action
“an agent composes a coherent, purchasable assortment that fits a retailer's profile, then iterates”
Reported outcome

Natural-language and image search reduce the distance between intent and discovery for retailers, saving them time; agentic coding tools provide meaningful offload of repetitive work, enabling engineers to scaffold complex screens in hours instead of days.

Reported metrics
time to scaffold Server-Driven UI screenhours instead of days
Retailer time to discover productssaving them time
Repetitive developer work offloadmeaningful offload on repetitive work
Reported stack
GitHub CopilotMCP serversPythonKotlin
Source
https://craft.faire.com/transforming-wholesale-with-ai-the-sequel-now-with-more-agents-9542f257dd45
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Natural-language and image search reduce the distance between intent and discovery for retailers, saving them time; agentic coding tools provide meaningful offload of repetitive work, enabling engineers to scaffold co…

What tools did this team use?

GitHub Copilot, MCP servers, Python, Kotlin.

What results were reported?

time to scaffold Server-Driven UI screen: hours instead of days; Retailer time to discover products: saving them time; Repetitive developer work offload: meaningful offload on repetitive work (source-reported, not independently verified).

How is this ecommerce ops AI workflow structured?

Retailer natural-language query → Parse query to structured filters → Return relevant products → Image upload for visual discovery → Background coding agent execution → Orchestrator dispatches sub-agents → Agent composes purchasable assortment.

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