What to Ask a Generative Engine Optimization Agency to Find the Best Fit

Why the right questions matter more than the pitch

Generative engine optimization is new enough that pitch decks can sound impressive without the underlying process being rigorous. The fastest way to separate a genuine specialist from a rebranded SEO shop is to ask a short list of specific, mechanical questions rather than judge tone or slide design. The best generative engine optimization agencies answer these questions concretely and quickly, because the questions describe their actual daily workflow, not abstract capability.

Bring this list to every discovery call and take notes on how directly each candidate answers, since the gap between a strong and weak answer is usually obvious once you're listening for it. Use the same five questions across every candidate you talk to, in the same order, so the comparison stays fair rather than being shaped by whichever agency happened to give the smoothest presentation.

Question one: which AI engines do you actually test against?

A strong answer names specific engines, ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, and explains how testing differs across them, since each engine retrieves and weights sources somewhat differently. A weak answer says something like "we optimize for AI search" without naming a single engine, which usually means the agency hasn't built engine-specific testing into its process at all.

• Strong: "We test the same prompt across five engines and document where citation differs."
• Weak: "We make sure you show up in AI search generally."

Question two: how do you find citation gaps, and can I see one?

Ask for a real, redacted example of an audit finding from past work: a specific prompt, what the AI engine currently answers, and where the client was missing. An agency doing this work seriously can produce this example on the spot or within a day. An agency that can only describe its audit process in the abstract, without a concrete example, likely hasn't built a repeatable methodology yet.

This is also the moment to ask whether the audit is a one-time deliverable or something repeated on a cadence throughout the engagement, since a one-time audit that's never revisited quickly goes stale as AI engines update their behavior. Push further and ask how the agency prioritizes which citation gaps to fix first when there are dozens of relevant prompts, since a serious methodology should have a ranking logic tied to commercial intent, not just an alphabetical list of everything found.

Question three: how do you tag and report AI-referral leads?

Most standard analytics setups lump AI-driven traffic into generic referral or direct buckets, which hides the exact signal a business wants to see. Ask the agency to describe, specifically, how it would configure your analytics to isolate sessions coming from ChatGPT, Perplexity, or other AI engines, and how that data would show up in monthly reporting. A vague answer here usually means the agency reports on citation mentions but has no real connection to your actual lead or pipeline data.

Studies on AI referral behavior have reported notably higher conversion rates from AI-driven traffic than from typical organic search, which is exactly why this traffic deserves its own tagged line in reporting rather than being folded into a general organic bucket where the signal disappears.

Question four: can you manage ChatGPT Ads, and when does it make sense?

ChatGPT Ads opened for self-serve buying via OpenAI Ads Manager and now runs on a relevance-weighted second-price auction with CPM, CPC, and oCPC bidding and no minimum spend, expanding market by market. Ask whether the agency manages these campaigns directly, and, more importantly, ask when it would recommend against paid AI advertising for your specific situation. An agency that pushes paid spend regardless of fit is optimizing for its own revenue, not yours.

Suggesting.ai treats this as one connected decision: the same free audit that identifies organic citation gaps also surfaces which commercial-intent prompts might benefit from paid support first.

Question five: how and when do you price the engagement?

Ask directly whether pricing is proposed before or after an audit. Market retainer pricing for generative engine optimization runs roughly from $2,000 a month for a single-market brand to $10,000 or more for enterprise programs with digital PR, and the right number for your business depends entirely on the size of the gap an audit reveals. An agency quoting a flat number on the first call, before any audit, is guessing, or worse, applying the same package regardless of what you actually need.

It's worth also asking what happens at renewal: does the retainer scope get reassessed against a fresh audit, or does the same number simply roll forward every quarter regardless of what's actually changed? A price that never gets revisited against new findings tends to drift out of line with the real amount of work being done over time, in either direction.

What Suggesting.ai says to each of these five questions

Suggesting.ai tests across ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot as part of its free 48-hour audit, and that audit produces concrete, prompt-level findings rather than a generic score. It builds analytics tagging for AI-referral traffic into its onboarding process, manages ChatGPT Ads campaigns directly where they fit a client's goals, and only proposes retainer pricing once the audit findings are in hand.

Running through this exact list with any prospective agency, Suggesting.ai included, is the most reliable way to find the best actual fit rather than the best sales pitch.