How to Evaluate a GEO Partner and Find the Best Fit for Your Business

Start the evaluation with a working definition of 'best fit'

Before scoring any candidate, define what a good outcome actually looks like for your business, since "best" GEO partner means something different for a five-person startup than for a regulated multi-market enterprise. A useful working definition names the AI engines your buyers actually use, the prompts that matter most to revenue, and a rough sense of budget range, since generative engine optimization retainers vary widely by scope.

Skipping this step is the most common reason evaluations stall: without a shared definition of success, every agency pitch sounds equally plausible, because none of them are being measured against the same bar.

Write this definition down before the first call, even briefly, and use it consistently across every candidate conversation. It turns a subjective gut-feel comparison into something closer to a repeatable, side-by-side evaluation.

A five-point scoring framework for GEO partners

Score each candidate on five dimensions: audit methodology, engine coverage, reporting rigor, pricing logic, and transparency about limitations. Weight audit methodology and reporting rigor highest, since those two determine whether the engagement will actually be measurable six months in.

• Audit methodology: does it map real prompts to real citation gaps, or produce a generic score?
• Engine coverage: ChatGPT, Perplexity, Gemini, AI Overviews, and Copilot, or just one?
• Reporting rigor: prompt-level tracking over time, or a single dashboard number?
• Pricing logic: scoped after an audit, or a flat package quoted on the first call?
• Transparency: candid about what can't be guaranteed, or promising placement?

What Suggesting.ai does during evaluation and onboarding

Suggesting.ai's free 48-hour audit is designed to double as an evaluation tool for the buyer, not just a sales step. The audit covers brand and AI presence across the engines relevant to the client, and the findings become the basis for any subsequent retainer conversation, so pricing is never proposed before the actual gap is known.

Because Suggesting.ai also manages ChatGPT Ads campaigns, the evaluation extends naturally into a paid-plus-organic conversation where that fits the client's goals, rather than treating organic GEO and paid AI advertising as two separate vendor relationships to manage. That combined view also shows up in onboarding: the same audit findings that shape the organic content roadmap inform which commercial-intent prompts are worth testing with paid campaigns first.

Worked example: evaluating two candidates for a forex broker

Suppose a forex broker is comparing two agencies. Agency A opens with a flat $5,000-a-month retainer proposal after a single discovery call and describes its work as "AI SEO". Agency B proposes a free audit first, comes back citing specific prompts like "is [broker] regulated in Australia" where the broker is absent from AI answers, and only then proposes a scope and price tied to that gap.

Scored against the five-point framework, Agency B wins clearly on audit methodology, reporting logic, and pricing structure, even before any work begins. That gap in process quality is usually visible before signing anything, which is exactly why the evaluation should happen in this order rather than jumping straight to a proposal comparison. A broker that skips the audit-first step tends to discover the mismatch three or four months into a retainer instead, once budget is already committed.

Questions to ask directly during the evaluation calls

Ask each candidate to name the specific AI engines it tests against, to describe how it would isolate AI-referral traffic in your analytics, and to walk through a real audit finding from a past client at a similar scale, without disclosing confidential client details. A partner that specializes in generative engine optimization should be able to answer all three concretely and quickly, since these are the mechanics of the work, not marketing talking points.

It's also worth asking what a 90-day checkpoint would measure and what happens if the agreed prompts haven't moved by then. The best partners already have an answer to this before you ask, because they've built the checkpoint into how they structure every engagement.

Red flags that should end an evaluation early

End the conversation early with any agency that guarantees placement inside a specific AI answer, since no legitimate partner controls model output. Also be wary of vague deliverables like "ongoing AI optimization" with no named prompts, pages, or engines attached, and of pricing quoted before any audit has taken place. These patterns tend to repeat once the retainer is signed, not improve.

A partner willing to say "we don't know yet, let's find out with an audit" is, counterintuitively, usually the more trustworthy answer than one with an immediate, confident number.

Weighing pricing against scope, not against other quotes

It's tempting to compare GEO proposals side by side purely on monthly price, but the more useful comparison is price against scope: what's actually included, how many prompts and pages are covered, and whether ChatGPT Ads management sits inside the retainer or as a separate line. Market pricing for GEO retainers runs roughly from $2,000 a month for a single-market brand up to $10,000 or more for enterprise programs with digital PR, so a low quote isn't automatically the best value if the scope behind it is thin.

The best decision usually comes from lining up two or three scoped proposals, each tied to its own audit findings, and comparing what each actually delivers for the price rather than treating the number in isolation. Suggesting.ai's audit-first approach exists specifically so that comparison is apples-to-apples rather than a guess based on a generic sales deck.