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E-commerce

Bundle Builder from Co-Purchases

Finds what customers actually buy together, proposes 3-5 margin-checked bundles, and drafts the listings — staged for the owner.

Best forEcommerceRetailServices
Agents9 required
Duration4-7 minutes

Turns a hunch about what sells together into an evidence-backed proposal set. It reads completed order history, computes co-purchase frequency and lift (returns-adjusted, trivial pairs filtered), and shapes the strongest pairs into at most 3-5 bundle candidates — each citing the co-purchase rate and order count behind it. Pricing runs through the margin policy with the math on the table; below-floor pricing is flagged, never silently proposed. Listing copy, bundle structure, and the rollout notes are drafted in full, then the whole package stages for the store owner — payloads and copy only, no store writes until the owner approves.

How it runs

Multi-agent orchestration — here's the flow, step by step.

01

Pull completed order lines for the window, with returns and cancellations noted so pair stats stay honest.

ecommerce scanner
02

Compute co-purchase frequency and lift; filter trivial pairs.

data analyst
02

Shape the strongest pairs into 3-5 bundle candidates, each with its evidence table.

upsell finder
03

Price each bundle within the margin policy, flagging below-floor results.

pricing analyst
03

Write the listing copy per bundle from the real component SKUs.

product description writer
03

Define the bundle structure — components, variants, image plan, catalog placement.

catalog optimizer
03

Voice pass across all bundle copy.

brand voice agent
04

Assemble the owner package with evidence tables and pricing math; dedupe against prior proposals.

workflow orchestrator
04

Stage the payloads and archive the proposal; nothing touches the store until approved.

distributor

Required Agents

9
  • ecommerce-scanner
  • data-analyst
  • upsell-finder
  • pricing-analyst
  • product-description-writer
  • catalog-optimizer
  • brand-voice-agent
  • workflow-orchestrator
  • distributor

Connections

Required

notionshopifyslack

Optional

shopifynotionslack

What it does

  • Co-purchase frequency and lift computed from real order history
  • 3-5 bundle candidates, each citing its evidence
  • Margin-checked pricing with the math shown per bundle
  • Below-floor pricing flagged, never silently proposed
  • Listing copy and bundle structure drafted per bundle
  • Returns-adjusted pair statistics
  • Idempotent per analysis window — proposals dedupe on rerun
  • Staged payloads only — no store writes without approval

Example prompt

Look at the last 90 days of orders, find what customers buy together, and propose a few bundles with pricing that protects my margin — draft the listings for my review.

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