# Willy Chavarria Uses AI Agents to Bundle Products in Real Time, Lifts BFCM Conversion *Agentic commerce platforms watch shopper behavior live and assemble product recommendations without human input.* By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC. Published 2026-10-23. Canonical: https://www.pops4.com/stash/articles/willy-chavarria-agentic-commerce-platforms-2026-10-23t00-6 Subject: Willy Chavarria / agentic commerce platforms Tags: bundling, ai, conversion, product recommendations, ecommerce, bfcm --- Fashion label Willy Chavarria deployed agentic commerce—AI systems that autonomously recommend and bundle products based on live customer behavior—during Black Friday Cyber Monday 2024 to increase conversion during peak traffic, according to Modern Retail. The platform watched individual shopper sessions in real time, identified purchase intent signals, and dynamically grouped complementary items without manual merchandising. The system tracked click sequences, dwell time, cart additions, and navigation patterns across the site. When a visitor browsed a specific jacket, the agent surfaced matching pants, accessories, or layering pieces based on prior purchase patterns and current inventory depth. The recommendations updated continuously as the shopper moved through the catalog. Willy Chavarria's team set guardrails—margin floors, inventory thresholds, brand coherence rules—but the agent decided which products to pair and when to present them. Agentic commerce works because it eliminates the merchandising lag that costs revenue during high-velocity events. Traditional bundling requires a marketer to predict demand weeks ahead, lock in groupings, and hope traffic behaves as forecast. When a product surges unexpectedly or inventory shifts mid-event, static bundles miss the opportunity. The AI agent recalibrates every session. If a colorway sells through, it rotates in the next-best alternative. If cart abandonment spikes on a bundle price, it tests a tighter grouping. The result is bundles that reflect live supply and live demand, not a guess from October. The mechanism also shortens the path to cart. A shopper browsing separates typically visits multiple product pages, deliberates fit across items, then adds one piece and continues searching. The agent surfaces the full outfit on the first product page, reducing clicks and decision friction. The visitor sees the complete look, confirms coherence, and adds all items in one action. Conversion increases because the next step is clearer and the mental work is lower. A small physical-product brand can run the same play with accessible tooling. Install an AI recommendation engine that integrates with Shopify or WooCommerce—platforms like LimeSpot, Rebuy, or Wiser offer agentic bundling features starting under **$300/month**. Configure the system to track session behavior: pages viewed, time on product, cart adds, search queries. Set inventory rules so the agent only recommends in-stock SKUs and enforces minimum margin thresholds to protect contribution. Define complementary product relationships manually at first—jackets pair with specific pants, mugs pair with coasters—then let the agent learn from conversion data which pairings actually close. Enable dynamic bundles on product pages and in cart: when a shopper adds a core item, the agent immediately suggests the add-on at a small discount or as a "complete the set" offer. Test bundle discount depth—start at **10 percent** off for two items, measure lift, adjust. Run the agent live during your next seasonal sale or product launch when traffic concentration justifies real-time optimization. Monitor average order value and units per transaction daily. If AOV climbs without killing conversion rate, the agent is working. The broader pattern is that merchandising becomes continuous rather than episodic. Brands that treat product groupings as a once-per-quarter creative exercise lose revenue to brands that let algorithms respond to what customers actually do in each session. ## The takeaway AI agents bundle products live based on shopper behavior, eliminating merchandising lag and shortening the path to cart during peak traffic. --- ## Publisher **Hako Shikin LLC** — Virginia Beach, Virginia. Founded 1997. ASI 217876 · DUNS 18-204-6339. Principal and author: **Jenny Huang Goodman MPA MSc MHSA**. - Author: https://www.huanggoodman.com/about - LLM context: https://www.pops4.com/stash/llms.txt - MCP endpoint, for AI agents: https://mcp.pops4.com/mcp - Client dashboard: https://dashboard.pops4.com/ - Catalogue: 70,000+ products, 200+ brands