# Willy Chavarria ran BFCM on autopilot with agentic AI pricing and inventory optimization *Autonomous systems adjusted pricing and stock in real time without manual intervention during the peak sales period.* By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC. Published 2026-10-25. Canonical: https://www.pops4.com/stash/articles/willy-chavarria-2026-10-25t00-2 Subject: Willy Chavarria Tags: dynamic pricing, inventory optimization, bfcm, agentic ai, discount strategy --- Willy Chavarria, the New York-based fashion brand, used agentic commerce systems to manage Black Friday and Cyber Monday without manual pricing or inventory decisions, according to Modern Retail. The AI ran autonomously, adjusting prices and stock allocation across sales windows based on real-time demand signals. The brand deployed AI agents that monitored conversion rates, inventory velocity, and competitive pricing continuously during the four-day sales period. When a product showed signs of selling out early, the system automatically raised the price incrementally to extend availability across the full promotional window. When slower-moving SKUs accumulated, the AI deepened discounts to clear stock before Monday close. No human approved these moves. The mechanism works because agentic AI separates decision-making from human reaction time. Traditional dynamic pricing requires a marketer to review dashboards, decide on a new price, and implement the change—a cycle measured in hours. Agentic systems operate in minutes, running thousands of micro-adjustments that a small team cannot execute manually. For physical product brands, this matters most when inventory is finite and the sales window is compressed. A sweater that sells out Saturday morning at **25% off** could have sustained traffic through Monday at **15% off**, capturing more margin. A jacket still in stock Monday evening at **25% off** should have been **35% off** on Saturday to move units when traffic peaked. The system also optimized inventory allocation between direct-to-consumer and wholesale channels. When the brand's own site showed higher conversion on a specific colorway, the AI reduced wholesale allocation and shifted units to DTC fulfillment. This cross-channel optimization requires real-time inventory visibility and the authority to reroute stock—capabilities most small brands lack in their ERP systems. For a small physical-product brand, the steal is accessible without enterprise software. Start with rule-based automation in your existing tools. In Shopify, set up automatic discount codes that trigger based on inventory thresholds: when a SKU drops below **20 units**, activate a **10% deeper** discount. Use Zapier or Make to connect your store to a Google Sheet tracking hourly sales velocity—when a product's sales rate drops **50%** below its day-one pace, the automation emails you a price-cut recommendation with one-click approval. This gives you agentic behavior in 90-minute cycles instead of real-time, which still beats daily manual review. The next layer is demand-based pricing within your promotional window. Instead of a flat **30% off** for four days, run **25% off** Thursday and Friday, **30% off** Saturday, and **35% off** Sunday and Monday. Use your store's traffic data from last year's BFCM: if Saturday typically delivers **40%** of weekend traffic, that's when to hold your pricing power. The final day is for clearance. This sequencing costs nothing and recovers margin on early converters. For brands with multiple SKUs, build a simple priority matrix: flag your top **10** SKUs by margin and your top **10** by unit volume. During the sale, check these **20** items every **3 hours**. If a high-margin item is selling faster than projected, raise its discount threshold (e.g., move it from **30% off** to **25% off**). If a high-volume item is lagging, deepen the cut by **5 points**. This manual agentic approach takes **15 minutes** per check and protects your most valuable inventory from blanket discounting. The pattern Willy Chavarria demonstrates is decisional automation—letting systems make pricing moves inside guardrails you set. The brand didn't invent new AI; it gave existing AI the authority to act. For small brands, that authority starts with rules: if this, then that. Build the rules now, test them on a low-stakes sale, and expand the automation as you gain confidence in the logic. ## The takeaway Set inventory-triggered discount rules and time-based pricing windows to automate margin recovery during compressed sales periods. --- ## 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