Agentic Commerce, Built in Miami
AI agents that handle inventory, pricing, win-back, and catalog quality — autonomously, at scale. Built on Square + Medusa.js + Claude.
Observe. Reason. Act. Learn.
Observe — Real-time Square data: inventory, orders, customer behavior, catalog performance.
Reason + Act — LLM-powered decisions executed autonomously via Square API and Medusa workflows.
Learn — Outcome feedback improves agent accuracy over time. Full decision audit trail logged.
Six Agent Types We Build
Inventory Reorder Agent
Monitors Square Inventory in real time. Calculates optimal order quantity from sales velocity, seasonality, and capacity. Places POs automatically; escalates above a configurable dollar threshold.
Dynamic Pricing Agent
Monitors Square Catalog pricing against competitor feeds, demand signals, and inventory levels. Adjusts within merchant-defined floor/ceiling guardrails per SKU.
Customer Win-Back Agent
Monitors Square Customer API for lapsed high-value customers (30/60/90-day recency). Triggers personalized email/SMS outreach with a Square gift card or discount code.
Catalog Quality Agent
Crawls Square Catalog for AI-unreadable listings: missing descriptions, low-res images, non-standard variants. Flags issues and auto-applies fixes via Square Catalog API.
Fraud Detection Agent
Monitors Square Orders for patterns associated with payment fraud, account takeover, and refund abuse. Triggers holds before fulfillment to reduce chargeback rates.
Replenishment Combo Agent
For consumable / subscription-adjacent businesses. Identifies customers whose typical repurchase interval has passed; sends Square-payment-enabled reorder links and adjusts inventory planning.
Square + GCP + n8n + Claude
Data Layer — Square Inventory/Orders/Customers/Catalog → GCP Pub/Sub → BigQuery for historical agent context.
Agent Layer — n8n orchestrates multi-step workflows. Claude (Anthropic API) handles LLM reasoning with tool use.
Action Layer — Agents execute through Square API, Medusa workflows, and external supplier APIs via HTTP nodes.
ROI Benchmarks
15–25% Overstock Reduction
Inventory reorder agents replace human judgment-based ordering with data-driven quantities. For a $20M retailer, this translates to $300K–$800K in recovered revenue annually.
12–20% Win-Back Rate
Personalized + Square gift card campaigns vs. 3–5% for generic email blasts. Tracks redemption back to Square Orders for measurable ROI.
2–4% Gross Margin Uplift
Dynamic pricing agents within merchant guardrails on high-velocity SKUs. Prices respond to demand and competitor signals in real time.
4–8% Revenue Recovery
Stockout elimination through real-time monitoring and automated reordering. The cost of stockouts averages 4–8% of revenue per event for retailers.
Frequently Asked Questions
Common questions about agentic commerce with Seeed.
Build Your First Commerce Agent
Tell us which operational problem to solve first. We'll scope an agent implementation and project the ROI.
Square-native data access
Agents read and write to Square's real-time APIs. Your data stays in Square.
Full decision audit trail
Every agent action is logged with reasoning. Merchant override is always available.
6–8 week first agent
Single-agent implementations ship fast. Multi-agent systems run 12–16 weeks.
ROI projected before scoping
We model expected revenue lift and cost reduction from your data before quoting.
Build Your First Commerce Agent
Tell us which operational problem to solve first. We'll scope an agent implementation and project the ROI.
Square-native data access
Agents read and write to Square's real-time APIs. Your data stays in Square.
Full decision audit trail
Every agent action is logged with reasoning. Merchant override is always available.
6–8 week first agent
Single-agent implementations ship fast. Multi-agent systems run 12–16 weeks.
ROI projected before scoping
We model expected revenue lift and cost reduction from your data before quoting.
