Agentic Commerce Optimization (ACO)
Agentic Commerce Optimization: Help AI Shopping Agents Discover and Understand Your Products
What Is Agentic Commerce Optimization?
Agentic Commerce Optimization (ACO) is the practice of improving product information and its delivery so AI shopping systems can accurately interpret, compare, and recommend products. It includes complete attributes, clear product identities and variants, relevant content, machine-readable formats, and access to accurate product and offer information.
Fegmo connects this work with the catalog operations that create, enrich, validate, and distribute product content. Better product information supports discoverability and conversion today while preparing your assortment for AI-led shopping.
Shoppers ask AI assistants such as ChatGPT, Gemini, and Perplexity for products that fit a particular need. A useful answer may depend on materials, dimensions, compatibility, ingredients, dietary attributes, or the differences between variants.
Thin descriptions, missing specifications, and conflicting values make those comparisons harder.
Thin descriptions obscure the detail agents need to evaluate suitability.
Missing specifications make comparison difficult across products and variants.
Conflicting values create uncertainty about what is accurate.
Inaccessible information is less likely to be correctly represented or considered.
Accurate, accessible product information is the foundation for AI shopping discoverability.
ACO CAPABILITIES
Build a Structured Product Foundation
Ingest and normalize fragmented, incomplete information from supplier files, spreadsheets, PDFs, images, feeds, and existing systems. Organize product identities, attributes, and relationships into a consistent catalog foundation.
Enrich Content for Discovery and Comparison
Create clearer titles and descriptions, fill attribute gaps using available source information, and generate product images and videos. Add the detail shoppers and AI agents need to judge suitability and compare products, while preserving accuracy and brand requirements.
Check proposed content against product data, brand guidelines, and channel rules. Apply confidence thresholds and approval policies, and route uncertain or sensitive changes to human review through a single review queue. Your team stays in control as catalog work scales.
Measure Catalog Readiness
See where product data falls short of what AI shopping systems need. Evaluate SKUs on content quality, crawlability, semantic quality, and contextual signals, and prioritize the gaps that matter most.
Distribute Through Supported Commerce Interfaces
Prepare and syndicate channel-ready listings and machine-readable product information through supported integrations, APIs, and feeds. Keep AI-facing information connected to the catalog content your team manages.
Give Your Catalog an Agent That Can Respond
Make governed product intelligence available through APIs and Model Context Protocol (MCP), so buyer-side agents can interact with the catalog in near real time. Product discovery and comparison are grounded in your catalog data.
Fegmo brings together work usually spread across PIM, DAM, attribute enrichment, media generation, validation, and syndication. Because AI-readiness work happens where the catalog is created and maintained, there are fewer handoffs, no duplicate review queue, and less risk of AI-facing data drifting from your approved product data.
Use Fegmo as your product data platform, or connect it to your existing systems and start with one priority assortment or workflow. Expand as you demonstrate value.
Retailers
Improve assortment completeness and make products easier to find and compare, including fresh, perimeter, and private-label categories where source content may be limited.
Brands
Create richer product information and media, reduce content-production work, and keep representation consistent across channels.
Marketplaces
Normalize fragmented seller content, identify quality gaps, and scale enrichment and validation across large catalogs.
Better Catalog Content Already Delivers Business Value
The Save Mart Companies observed 17% higher digital sales across Fegmo-enriched products during initial image-enrichment work. Fegmo also supports paid enterprise work across a sports and activewear portfolio and a major European marketplace.
That experience informs our approach to ACO: improve the underlying product information, automate the work needed to maintain it, and connect governed catalog intelligence with new shopping interfaces.
What is Agentic Commerce Optimization?
How does ACO differ from SEO?
Do we need to replace our PIM or DAM?
Can Fegmo interact with AI shopping agents?
Which AI shopping destinations can we connect to?
Does every AI-generated change require manual approval?
Does ACO guarantee that our products will be recommended?
How should we measure progress?
How do we get started?
See how Fegmo can improve product information, automate catalog work, and connect your assortment with buyer-side agents.