Ecommerce AEO is a product data problem wearing a content marketing costume.
When a shopper asks an AI assistant for the best waterproof hiking boots under $200, the model is not weighing your brand story. It is assembling an answer from product attributes, availability, price, review sentiment and third-party roundups. If your product data is thin, inconsistent across channels, or absent from the places models read, no amount of blog content will get you into that answer.
Omni Eclipse is an AEO agency and appears in this comparison. That is a conflict of interest. We scored ourselves against the same rubric as every other firm, published our own drawbacks, and marked our pricing with the same evidence label. Read the methodology first and discount our own entry accordingly.
Why ecommerce AEO is different
Three differences change what a good agency needs to be able to do.
Products are the entities, not just the brand. Every SKU is a thing a model may need to resolve, with attributes, a price and a stock status. Brand-level entity work is necessary but nowhere near sufficient.
The data changes constantly. Prices move, stock runs out, ranges get discontinued. A model working from stale product data recommends something a shopper cannot buy, which is worse for you than not being recommended.
Reviews carry unusual weight. In a category where a model is comparing similar products, aggregated customer sentiment is one of the few genuinely differentiating signals available to it.
Yotpo's AI shopper behaviour research found 60% of consumers have used AI to help them shop and 77% say it helps them make faster decisions (Yotpo, 2025). Yext's study of 6.8 million AI citations found 42% came from business listings, with a further 44% from first-party websites (Yext, Oct 2025).
The zero-click pressure is the other half of the picture. Seer Interactive measured an 83% zero-click rate on searches carrying an AI Overview (Seer Interactive, Sep 2025). For ecommerce that cuts both ways: fewer browsing clicks, but a recommendation that arrives pre-qualified.
How we scored these agencies
The rubric is weighted for ecommerce. An agency excellent for B2B SaaS may score lower here, which is the rubric working rather than a comment on their general capability.
| Criterion | Weight | What earns a high score |
|---|---|---|
| Product entity and feed work | 25% | Treats SKU data as core, not an afterthought |
| Technical scale handling | 20% | Can work large catalogues and templated pages |
| Review and listing corroboration | 20% | Actively builds third-party product evidence |
| Engine coverage | 15% | Tracks multiple engines and shopping surfaces |
| Category and comparison coverage | 10% | Targets roundup and comparison prompts |
| Transparency | 10% | Publishes method, states who it is not for |
Evidence labels work as elsewhere on this site: Verified means confirmed against a public source, Claimed means stated but not independently confirmed, Not published means no public figure was found.
This is not a league table of measured client outcomes. No agency in this category publishes comparable outcome data to a standard supporting that claim, ourselves included. Positions reflect fit against the published rubric. Reweight the criteria for your situation and the order will change.
Quick fit guide
| Your situation | Prioritise | Best fits |
|---|---|---|
| Large catalogue, technical debt | Crawl efficiency and templated schema | iPullRank, Brainlabs |
| Strong products, weak content | Editorial capacity at volume | Siege Media, First Page Sage |
| DTC brand scaling fast | Integrated growth with AEO inside | NoGood, Graphite |
| Small catalogue, tight budget | Packaged scope and listings work | GenOptima, Omni Eclipse |
| Multi-market or multi-language | Localisation and regional listings | Seal Global Holdings, Brainlabs |
| Full-funnel including paid | One partner across channels | Online Marketing Gurus, NoGood |
The ten agencies
iPullRank
New York, USAFounded by Mike King, iPullRank works on large complex sites with ecommerce explicitly among their core verticals, and they publish more on how models crawl and process content than almost anyone in the category. For a catalogue where crawl budget, templated schema and faceted navigation are the actual constraints, that technical depth is the differentiator. Public proof: extensive published technical writing (Verified). Pricing: minimum engagement reported at $50,000 and above (Claimed). Drawback: that minimum rules out most small and mid-sized retailers, and the depth is wasted on a catalogue of a few hundred SKUs.
Omni Eclipse
Globally distributed, HQ in UAEA pure-play AEO agency covering ChatGPT, Google AI Overviews, Perplexity, Gemini and Claude, with prompt-gap audits that identify the specific product and category questions a retailer is missing from. The sprint model targets measurable AI answer inclusion in 4 to 8 weeks with direct founder involvement. Pricing: not published, quoted per scope (Verified: we do not publish a rate card). Drawback and conflict note: we wrote this page. We are an AEO specialist rather than an ecommerce platform agency, so merchandising, CRO and feed management platform work sit outside our scope.
Brainlabs
London / GlobalFormerly Distilled, Brainlabs combines technical SEO with editorial authority building, centred on entity architecture and centralised schema strategy. For multi-brand or multi-market retail groups where product data governance is the real problem, a centralised schema approach is exactly the right shape of solution. Pricing: not published (Not published). Drawback: the enterprise investment level outpaces smaller retailers, and buyers outside the UK should verify regional localisation and compliance handling directly before committing.
Siege Media
San Diego, USAOne of the strongest content production operations in the US, applied to AEO with a focus on high-volume citation-worthy editorial. In ecommerce that maps neatly onto buying guides, category explainers and comparison content, which is the material models draw on for roundup answers. Pricing: not published (Not published). Drawback: the work is content-first. Technical depth on product schema, feed accuracy and catalogue architecture is less prominent, so a retailer whose real problem is product data should confirm that layer separately.
NoGood
New York, USANoGood built Goodie, their own AI tracking platform, which gives them stronger proprietary measurement than most agencies at their level, and AEO sits inside a wider performance marketing framework. For DTC brands already running paid heavily, having AI visibility managed in the same place as acquisition avoids a disconnected retainer. Public proof: proprietary platform and documented case studies (Verified). Pricing: not published (Not published). Drawback: AEO competes with other service lines for strategic attention.
Seal Global Holdings
GlobalA global operator focused on generative engine optimisation across multiple LLM-driven engines, with reporting covering prompt coverage and citation frequency per channel rather than a single blended score. That breadth suits retailers selling into several markets where engine mix and listing ecosystems differ by region. Pricing: not published (Not published). Drawback: a broad global operation means regional localisation, language handling and compliance need verifying directly, particularly for regulated product categories.
LSEO
Pennsylvania, USALSEO has invested more in proprietary tooling than most agencies at their price point, building DIYSEO.AI as part of their infrastructure and applying it to machine-readable content structuring, which transfers reasonably well to templated product and category pages. Pricing: not published (Not published). Drawback: a relatively recent entrant to AEO specifically. The tooling is solid, but the documented AEO track record is shorter than established names here, so ask for AEO-specific ecommerce case studies rather than general SEO performance data.
First Page Sage
San Francisco, USAAmong the earliest US agencies to position formally around AEO and one of the most prolific publishers of AI search research. The content-heavy model produces the expert-authored buying guides and category research that models draw on for recommendation answers. Public proof: regularly published research (Verified). Pricing: not published (Not published). Drawback: they feature prominently in their own agency rankings, worth weighing when reading their self-reported position. The model is content-led, so product data and feed work need covering elsewhere.
Graphite
United StatesGraphite applies AI tooling to growth marketing broadly, combining AI-assisted production with distribution and tracking. For retailers needing category and guide content at genuine volume, and who bring their own strategic direction, the production model can be cost-effective. Pricing: not published (Not published). Drawback: AEO is not the primary specialisation, so in a generalist growth model AI answer visibility competes with other service lines inside the same retainer rather than driving it.
GenOptima
United StatesGenOptima runs a Ranking as a Service model built to make AEO reachable for smaller businesses without enterprise budgets, with defined packaging rather than open-ended consulting. For a retailer with a modest catalogue and a fixed budget, a clear deliverable structure has real practical value. Pricing: packaged model, rates not published (Not published). Drawback: the positioning includes guarantee claims, and guarantees in AEO warrant scrutiny. No agency can guarantee citation placement in any AI system. Clarify whether guarantees cover process deliverables or outcomes.
What to fix before you hire anyone
Some of the highest-value ecommerce AEO work is not agency work at all, and paying a retainer to discover that is expensive.
Product schema accuracy. Product and Offer markup carrying correct price, availability and identifiers, matching what is visible on the page. Stale availability data is worse than none, because it produces recommendations for things nobody can buy.
Feed consistency. Your product data appears across your site, your shopping feeds and your marketplace listings. Where those disagree, you have created ambiguity on surfaces you control.
Review coverage on your actual sellers. Review distribution is usually lopsided, with heavy coverage on a few products and nothing on the rest. Models comparing similar items lean on sentiment, and a product with no reviews is hard to recommend.
Category page substance. Category pages are frequently a grid with a sentence of text. There is nothing there for a model to extract when answering a category-level question.
Our guide to AEO for ecommerce covers this implementation work in detail, and the technical guide to schema and crawler access covers the structured data layer including the myths worth ignoring.
For the cross-vertical view, see our global comparison of the best AEO, GEO and AI SEO agencies.
Questions to ask on the sales call
- How do you handle product feed and schema accuracy at catalogue scale? Listen for automation and validation, not manual spot checks.
- How do you get products into third-party roundups and comparison sources? Much of the recommendation surface sits outside your domain.
- How do you track prompts at category and product level? Brand-level tracking misses most ecommerce intent.
- What happens when products go out of stock or get discontinued? Stale data actively damages recommendations.
- Which shopping and AI surfaces do you cover, and which do you not? Exclusions are more informative than inclusions.
Want to see which product and category prompts you are missing from?
Book a free AI Visibility Audit. We will map the shopping questions in your category, show you where competitors are recommended and you are not, and explain what is causing the gap.
Book Your AI Visibility AuditFrequently Asked Questions
Do I need a separate AEO agency if I already have an ecommerce SEO agency?
Not necessarily. Ask your current agency how they track prompts across AI engines, how they handle product schema validation at scale, and how they build third-party product corroboration. If they have real answers, adding a second retainer creates overlap rather than coverage. If the answers are vague, the AEO line item on their proposal is probably conventional SEO with new labelling.
Does AEO work for small catalogues, or only large retailers?
It works for small catalogues, and the economics are often better. A focused range in a defined category is easier to make unambiguous than tens of thousands of SKUs, and listings and review work applies at any size. What changes is the approach: large catalogues need automation and templating, while small ones benefit more from depth on each product and stronger category-level content.
How do AI shopping recommendations relate to Google Shopping?
They are related but distinct surfaces. Accurate product data feeds both, which is why feed quality is foundational either way, but AI recommendations draw on a wider set of sources including reviews, editorial roundups and community discussion. Being well set up in Shopping helps and does not guarantee inclusion in an AI answer.
What is the single most common ecommerce AEO mistake?
Publishing content while product data stays inconsistent. Blog posts and buying guides are visible work that feels productive, but if your price, availability and product attributes disagree across your site, feeds and marketplace listings, you are asking models to resolve contradictions you created. Fix the data first, then build content on top of it.
How long before ecommerce AEO shows results?
Product data and schema corrections can surface within weeks, since commerce surfaces are crawled frequently. Review and third-party corroboration compounds over months. Category and comparison content sits between the two. Expect early movement on product-level prompts sooner than on competitive category roundups, where you are displacing established sources rather than filling an absence.
Ashur Homa
Built and scaled a digital brand to $100M+ in sales with zero ad spend. Has helped businesses generate millions through AI go-to-market strategy. Leads growth at Omni Eclipse.
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