GEO consulting for consumer brands.

GEO consulting, or generative engine optimization consulting, is the work of getting a brand recommended inside AI-generated answers rather than ranked on a search results page. For consumer brands it covers structured product data and schema, machine-readable content, bot governance, retailer and review-platform presence, and ongoing measurement of how often AI assistants cite the brand versus competitors. At Shelf Help AI the diagnosis is the AI Visibility Audit, the implementation is Agentic Commerce Readiness, and the measurement is the monthly Share of Model Tracker.

In practice the two terms are used interchangeably, and most practitioners will tell you the same. Where a distinction is drawn, AEO, answer engine optimization, usually refers to the technical side of making a site machine-readable and quotable: structured data, crawl access, definitional writing, clean content structure. GEO, generative engine optimization, is used more broadly for the whole job of earning recommendations inside generated answers, including entity strength, third-party citations, retailer presence, and measurement. Shelf Help AI treats the AEO technical audit as one component inside GEO consulting rather than as a separate service.

SEO competes for a click on a results page; GEO competes to be named inside a generated answer, where there may be no click at all. They share fundamentals such as crawlability and clean site structure, then diverge sharply. GEO leans much harder on structured data, extractable product attributes, definitional first sentences, entity consistency across the web, and third-party signals. Success is measured as citation rate and position within an answer rather than rank and sessions.

At Shelf Help AI: the AI Visibility Audit is $1,950 fixed, Agentic Commerce Readiness implementation runs $3,000 to $8,000 depending on scope, and the Share of Model Tracker is $295 per month with no minimum term. Most brands begin with the audit plus three months of tracking at $2,595. For context, agencies serving enterprise clients in this space publish considerably higher figures; iPullRank lists its AI Search Strategy program at "starting at $15,000/month" and NoGood states its average retainer is above $20,000 per month. Those are appropriate for large brands with dedicated budgets and are not what Shelf Help AI competes on.

The technical fixes can be implemented in weeks, but the models need to re-crawl and, in some cases, re-train before the change shows up in answers. Expect a first read on movement at 30 to 60 days after fixes ship, and a clearer trend at 90 days. This is the main argument for tracking rather than a single re-test: without a monthly trend line you cannot separate a real improvement from ordinary answer variance.

No, and treat any firm that guarantees it with suspicion. What can be committed to is the diagnosis and the work: you will know exactly where you stand across the major assistants, what is causing it, and what to fix in what order, and the fixes will be implemented properly. Whether a given model names your brand next month depends on factors no vendor controls, including how the model is trained and what your competitors do.

Not necessarily, and it is worth deciding deliberately rather than by default. Monitoring platforms such as Profound, AthenaHQ, Scrunch AI and Otterly.ai publish entry prices in the range of roughly $99 to $295 a month, with the useful tiers generally higher and some assistants sold as add-ons. They are genuinely good at watching a number continuously. If your brand needs that, we will say so plainly, including when a platform is the better purchase than our tracker. What a tool will not do is tell you what the number means for your category or sequence the fixes for you.