Agentic Commerce for Retail, Marketplaces & Online Brands

Agentic commerce is AI that can complete commerce tasks autonomously — discover products, compare options, answer questions grounded in your catalog, initiate checkout, handle returns, and escalate edge cases to humans. A chatbot answers questions. An agent does the thing. The difference matters because production agents need tool use, RAG grounding, guardrails, observability, and eval harnesses that chatbots do not.

No. We explicitly build around your existing platform. Shopify’s Storefront MCP, Customer Accounts MCP, and Checkout MCP let us connect agents to your catalog and orders without rewrites. For Magento, BigCommerce, commercetools, and custom stacks, we use headless APIs and embedded widgets. Average production deployment touches 8–15 systems; we do that integration work.

Tight RAG grounding in your live catalog, hybrid retrieval (BM25 + dense vectors) for accurate matching, output validation against source documents, and topic guardrails that refuse to answer outside known data. Production target is < 5% hallucination rate, measured continuously. Anything above that is a brand-risk incident, and we design for that reality from day one.

A focused pilot — one use case, one channel — typically ships in 8–12 weeks from kickoff to shadow deployment. Full production rollout adds 2–4 weeks of real-traffic tuning. Complex multi-channel or multi-agent platforms take 4–9 months. We always start with a scoped pilot that delivers measurable value before expanding.

Yes — three different protocols power this. OpenAI + Stripe’s Agentic Commerce Protocol (ACP) runs ChatGPT Instant Checkout. Google + Shopify’s Universal Commerce Protocol (UCP) powers Google AI Mode, Gemini, and Microsoft Copilot. Anthropic’s Model Context Protocol (MCP) powers Claude. We implement all three so your catalog becomes sellable inside every agent channel through a single integration layer.

This is what Stripe’s Shared Payment Tokens (SPTs) and Stripe Radar’s agent-traffic fraud model were built for. SPTs let an agent charge a saved payment method without ever seeing the credentials, and Radar’s fraud signals are now tuned for agent traffic (because human-fraud signals are obsolete). We implement ACP with these primitives from day one — not as an afterthought.

Documented production outcomes: 34% conversion lift and 18% AOV lift on AI-engaged sessions, 42% reduction in WISMO tickets, 42% increase in repeat purchases, 6–10% revenue lift from personalized merchandising, 60% fewer pricing errors. Industry benchmark is $3.50 return per $1 invested, top performers hit $8 per $1. We wire conversion lift, AOV, deflection, CSAT, and cost-per-resolution into a live dashboard so ROI is visible in real time.

This is where our financial-services background matters. We build audit trails, disclosure injection for regulated categories, jurisdictional returns logic (EU 14-day cooling off, US state-level variance), GDPR consent management for personalization data, and PCI scope containment for payment-completing agents. Most retail agencies skip this. We treat it as table stakes.

Agentic flows can chain 20–50 LLM calls per request. Without monitoring, monthly bills surprise everyone. We use model routing (cheap models for classification, expensive for generation), aggressive caching, prompt compression, and small-model fine-tuning for high-volume narrow tasks. Every deployment includes per-conversation cost dashboards and monthly budget alerts.