Agentic Commerce Optimization
Your products, inside every Agentic recommendation.
The 11-step framework to get your catalog found, canonicalized, and recommended by ChatGPT, Gemini, Perplexity, and every major AI shopping agent.
The Shift
20 years of keyword optimization built for humans. AI agents think differently.
LLM answer engines are good at matching shopper intent to products — but merchants haven't fed them enough product-level data to keep up. Over-optimized for keywords, under-optimized for agents.
Agents don't respond to storytelling, imagery, or in-store experiences. They evaluate structured signals: product facts, prices, availability, eligibility, policies, and expected outcomes. If your differentiation isn't machine-legible, it's effectively invisible.
of e-comm executives have improved product content quality in response to AI search growth
Salesforce
of merchants have an agent-readable product feed
Industry avg.
have an MCP server — the gap between intent and deployed capability is massive
Industry avg.
"Every unanswered gap in your product catalog is a doorway to a competitor."
— Andrew Bell, VP Research, ReFiBuy
The Framework
The 11-Step ACO Playbook
Originally a 9-step playbook, extended in July 2026 with two new steps covering multi-surface context capture and recursive catalog loops.
Infrastructure Access
Unblock AI crawlers (GPTBot, OAI-SearchBot, ChatGPTUser) at robots.txt, DNS, CDN, and WAF layers.
Crawlability
Ensure full catalog crawlability. Adopt llms.txt and schema.org metadata so JS-rendered content isn't invisible to agents.
Basic Product Content
Hero images, title/subtitle, price, offers, short and long descriptions — the non-negotiable baseline.
Variation Canonicalization
Parent/child (size/color) variation data canonicalized correctly. One of the most common silent failure modes.
Basic Attributes
Verbose, expanded attribute content. Verbosity is now rewarded — reversing 20 years of SEO brevity norms.
Expanded Attributes
Feature/benefit framing, use-case content, pairing suggestions — the content that turns impressions into recommendations.
Extra Content
Reviews, Q&A, multi-lingual handling, and accurate price/stock signals. Freshness directly drives agent confidence.
Product Card Monitoring
Ongoing tracking as AI engines update models 2–4× per year and competitors iterate. Optimization never stops.
Digital AI Shelf
Competitive share-of-shelf tracking across engines — the AI-era equivalent of traditional digital shelf analytics.
Multi-Surface Context Capture
Capture product-level context from every agentic surface: answer engines, retailer agents, on-site search, social, and offline.
Recursive Catalog Loops
Weekly Context Capture → Update Catalog → Publish loop. Answer-engine data shifts within a week of a catalog change.
The Recursive Catalog Loop
Answer-engine data shifts within ~one week of a catalog change, while the underlying models retrain over ~six months. The compound effect is significant: a weekly Context Capture → Update Catalog → Publish loop means 18 optimization cycles before a single model update. The merchants running this loop today will be impossible to displace by year-end.
Our ACO Services
What we do for you
Brandleap applies the full ACO framework to your catalog — from initial audit to continuous shelf optimization.
ACO Readiness Audit
We run a complete diagnostic of your catalog's AI visibility — bot access, crawlability, canonicalization, content gaps, and live shelf position across ChatGPT, Gemini, and Perplexity.
Catalog Optimization
We enrich your product data with the structured signals AI agents actually evaluate: verbose attributes, use-case framing, variation canonicalization, and schema markup.
Ongoing AI Shelf Management
We run the weekly recursive loop — capture what agents say about your products, close content gaps, republish, and track share-of-shelf versus competitors across every major engine.
13 Common ACO Pitfalls
Blocked bots, bad canonicalization, JavaScript-hidden variations, wrong PDP linking, stale price/stock signals, title discrepancies causing silent de-listing — the ACO framework documents 13 specific failure modes, each with documented real-world examples. We audit for all 13 on day one.