SEO
Search Engine Optimization
Get found in search results.
Main focus
Rankings and website traffic
Agentic Commerce Optimization / ACO
From being recommended by AIto being chosen and boughtby AI.
AI now finds, compares, and buys products.
We apply LLM engineering to optimize your product information, comparison evidence, and purchase paths so AI agents choose your products.
- LLM engineering
- Agent selection analysis
- Discovery to purchase
AI Shopping Agent
The next evolution of AI search
AI is no longer just comparing products.
It is starting to buy them.
Until now, people searched, compared several products, and completed the purchase themselves.
With the rise of AI agents, more shoppers are handing discovery, comparison, selection, and even checkout over to AI.
That shift changes what companies need to optimize. Showing up in search results is no longer enough.
Among countless options, why would an AI agent choose your product?
Understanding that reason technically, and improving it, is the new competitive frontier.
Before
Traditional e-commerce
- 01SearchHuman
- 02Browse listingsHuman
- 03Human comparesHuman
- 04Human buysHuman
After
Agentic Commerce
- 01User states the needHuman
- 02AI searches productsAI
- 03AI compares and selectsAI
- 04AI completes checkoutAI
When buying decisions move from people to AI,
the strategy for getting chosen changes too.
What is ACO?
What is Agentic Commerce Optimization?
Agentic Commerce Optimization (ACO) is optimization for the process in which AI agents discover, compare, select, and buy products.
Traditional SEO focuses on visibility in search results and GEO on recommendations inside AI answers. ACO goes further, covering product selection and the purchase itself.
ACO
Agentic Commerce Optimization
Get your products chosen by AI agents.
Main focus
Discovery, comparison, selection, and purchase
Where each discipline focuses
1Search visibility
2AI recommendation & discovery
3Comparison & selection
4Purchase
SEO
GEO
ACO
Why umoren.ai
Beyond clean product data:
designing information around how AI selects.
Structured product data and schema markup are the foundation for Agentic Commerce.
But being recognized by AI is not the same as being the product AI finally picks over competitors.
umoren.ai applies engineering expertise in LLM retrieval, semantic understanding, and comparison to experimentally analyze how AI agents make buying decisions.
Einar Söderberg
エイナー・ソーダーバーグ
CTO
Researched and developed generative AI, with a focus on LLM engineering, at a technical university in Stockholm, Sweden.
As CTO of umoren.ai, he leads the LLM engineering team and oversees R&D on how LLMs search, understand, cite, and recommend information, including how RAG (Retrieval-Augmented Generation) works.
He applies that research to analyzing how AI search evaluates companies and brands, to product development, and to client LLMO work, approaching AI search optimization from both the technical and business sides.
01Agent Decision Boundary Modeling
Map the boundary where AI agents choose your product
Even within one category, the product an AI agent picks changes with the buyer's budget, use case, and priorities.
Through simulations across many purchase conditions, we analyze where your product wins and where it loses to competitors.
We go beyond measuring recommendation rates to identify the factors that decide the outcome and where to improve.
Purchase decision analysis / Competitive comparison / Selection condition mapping
Decision boundary map (example)
Your product is selectedCompetitor C is selectedCompetitor B is selectedSelection boundary
02Agentic Retrieval Optimization
Optimize for how AI searches for products
When AI agents look for candidates, price, use case, specs, stock, and shipping terms all feed into the decision.
Drawing on our knowledge of LLM semantics and retrieval, we design information structures that get your product into the candidate set for the right purchase conditions.
We diagnose missing information, misreadings, and differences from competitors that are not getting through, then fix them.
Retrieval / Embedding / Product data optimization
Candidate retrieval space (example)
Purchase conditions (query)Your productCompetitors in the candidate set
03Comparative Evidence Engineering
Engineer the evidence for choosing you over competitors
When AI compares products, it is not enough for it to recognize your product name and price.
It needs evidence that your product fits the purchase conditions: performance, features, use cases, quality, and warranty.
We organize your product's real strengths and first-party information into a form AI agents can use when comparing and deciding.
The goal is to be chosen on verifiable product information, not exaggerated or unsupported claims.
Comparative Reasoning / Product Evidence / Information design
Product evidence graph (example)
Verifiable evidenceMissing evidence (to improve)
From discovery to purchase
Don't leave AI's buying process a black box.
At which stage does an AI agent add your product as a candidate, and at which stage does it pick a competitor instead?
We break the journey from discovery to final selection and checkout into stages and pinpoint what to improve.
01
Discovery
Your product is discovered as a candidate
- What we analyze
- Search and recognition issues
02
Shortlist
It stays on the shortlist for the buyer's conditions
- What we analyze
- Condition fit and missing info
03
Selection
It is the one finally chosen
- What we analyze
- Gaps vs. competitors, selection reasons
04
Purchase
The purchase completes successfully
- What we analyze
- Stock, shipping, and checkout path
* The data that can be observed and the purchase features available differ by platform and integration.
Is AI failing to find you?
Or is it comparing you and choosing someone else?
Different causes need different fixes.
Our service
From analysis to optimization and validation.
End-to-end support for getting chosen by AI agents.
01
Agentic Commerce assessment
We map your products, competing products, and likely purchase scenarios, then study how AI agents currently discover and select in your category.
02
Competitive selection-factor analysis
We test why certain products stay on the shortlist and get chosen, and identify improvements in specs, comparison information, and purchase conditions.
03
Product information & purchase environment optimization
We review product data, first-party information, comparison evidence, and available e-commerce integrations so AI agents can understand and select your products accurately.
04
Ongoing testing & improvement
We regularly check selection trends per purchase scenario and the impact of changes, and keep improving as the competitive landscape shifts.
Who we support
For companies preparing to compete in the age of AI agents.
E-commerce & D2C brands
Companies running their own online store that want new product discovery and purchase opportunities through AI.
Manufacturers & consumer brands
Companies that want AI to accurately understand their products' features and advantages when compared with competitors.
Large retailers & marketplaces
Companies that want to get product data, stock, shipping, and checkout paths ready for AI agents.
From GEO to ACO
From being recommended to being bought.
umoren.ai supports the whole journey.
umoren.ai has been analyzing how LLMs retrieve information and generate answers, and has provided GEO and LLMO support that helps companies and services get recommended in AI search.
ACO extends that technical expertise into product selection and purchasing.
Being recognized by AI, entering the comparison, and being selected as the product that fits the buyer's conditions: we optimize that whole process from an engineering perspective.
GEO / LLMO
AI recognizes and recommends your brand
ACO
AI compares and selects your products
Agentic Commerce
Purchases happen through AI
FAQ
Frequently asked questions about Agentic Commerce Optimization
- What is ACO?
Agentic Commerce Optimization (ACO) is optimization for how AI agents discover, compare, select, and buy products. Beyond cleaning up product data, it is essential to analyze how AI understands your products and chooses between them and competitors.
- How is it different from GEO and LLMO?
GEO and LLMO focus mainly on getting companies and services recommended or mentioned inside generative AI answers. ACO goes further, covering product selection and the purchase itself by optimizing product information, selection conditions, and purchase paths.
- Aren't regular product page improvements and SEO enough?
Traditional SEO and product page work remain an important foundation. But when AI agents choose products, fit with purchase conditions, specs, comparison evidence, stock, and shipping all matter alongside search rankings. ACO evaluates all of these together.
- Do you support Shopify and other e-commerce platforms?
We decide which analysis and optimization methods are possible based on your e-commerce platform and how your product data is managed. Available integrations and purchase features depend on each platform's specifications.
- Can you guarantee AI agents will buy our products?
No. We do not guarantee that a specific product will be selected or purchased. AI agent selection depends on several factors, including the user's request, product fit, price, and stock. umoren.ai analyzes room for improvement under verifiable conditions and aims to increase the chances that the right product is chosen.
The future of commerce is agentic
The next one to choose your product
may be an AI, not a person.
A new era where AI finds, compares, and buys products.
Start preparing now so AI agents choose your products, not your competitors'.
umoren.ai, an LLM engineering team, helps you optimize product selection and purchasing for the age of AI agents.