How to shortlist the best answer engine optimization agencies

Why shortlisting AEO agencies is harder than shortlisting SEO ones

Traditional SEO agency shortlisting has decades of shared vocabulary behind it — rankings, backlinks, domain authority — so buyers already know roughly what to ask. Answer engine optimization is new enough that even the term itself overlaps with GEO, LLM SEO, and AI visibility, and vendors use these labels inconsistently.

That means a shortlisting process built for traditional SEO procurement will miss the questions that actually separate a strong AEO vendor from a rebranded content shop. The process needs its own checklist, built around how AI citation actually works rather than borrowed wholesale from search engine ranking factors.

Committees that skip this step often end up comparing five proposals that all use the word "GEO" but describe five different actual services underneath it, which makes the final decision come down to whichever proposal reads most confidently rather than whichever one actually covers the ground your brand needs covered.

A five-step process for building the shortlist

Start with a documented list of five to eight candidates sourced from more than one channel — referrals, search, and direct outreach — rather than relying on a single recommendation. Then apply a consistent process to every candidate before narrowing.

• Step 1: Request a written scope document from each candidate, not just a sales call summary
• Step 2: Ask every candidate the same set of specific technical and measurement questions
• Step 3: Run the same live prompt test with each finalist and compare their diagnostic depth
• Step 4: Score each candidate against a fixed checklist, weighted by what matters most to your buyer journey
• Step 5: Check for a free or low-commitment first step, like an audit, before signing anything long-term

The point of this structure isn't bureaucracy for its own sake — it's removing the influence of whoever gave the best sales pitch, so the decision is made on comparable evidence. Keep the same five questions and the same scoring sheet for every candidate; the moment one vendor gets a different set of questions, the comparison stops being fair.

Red flags that should drop a candidate off the shortlist

Cut any candidate who can't clearly separate organic GEO work from paid ChatGPT Ads management in their proposal — conflating the two usually means neither is being scoped seriously. Also drop anyone unwilling to name the specific AI engines they measure, since "AI visibility" without naming ChatGPT, Perplexity, Gemini and Google AI Overviews specifically is a vague claim.

Be equally cautious of a candidate that guarantees a specific ranking or citation outcome — no one can honestly guarantee AI output, and a guarantee like that is more often a sign of an inexperienced or overselling vendor than genuine confidence.

Finally, drop any agency that can't explain their crawler-access review process in plain terms during the first call — this is core technical groundwork, not an advanced add-on, and an agency fuzzy on it now will be fuzzy on it in the retainer. If a candidate's answer to any of these questions changes noticeably between the first call and the written proposal, treat that inconsistency as a scoring signal in itself — it usually matters more than whichever candidate talks the loudest about being the best in the room.

What Suggesting.ai brings to a shortlist committee

Suggesting.ai fits naturally into this kind of structured process because its first deliverable — a free 48-hour audit covering citation status, crawler access, and competitor share of voice — gives a committee something concrete to score, rather than a sales narrative to take on faith.

Because the audit costs nothing and takes two days, it can sit inside a shortlist process as a low-risk step for every finalist, rather than something reserved for the vendor eventually chosen. That lets a committee compare actual diagnostic output side by side across two or three finalists before any contract is signed.

It also means the paid-versus-organic scoping conversation happens with real findings on the table, not before anyone knows what a given brand actually needs. For a shortlist committee juggling several finalists, that's often the deciding factor — one candidate arrives with hard evidence, the others with a pitch.

Worked example: shortlisting for a forex broker

A forex broker's shortlist committee should specifically test each finalist on a prompt like "best regulated broker for [specific market] with tight spreads," since that's the bottom-funnel query type most relevant to a broker's pipeline. Whichever finalist gives the most specific, evidence-based answer about the current citation gap deserves the highest score on that line item.

Suggesting.ai's client base already includes brokers and finance media — Economies.com, FxNewsToday.ae, InvestingTrading.com among them — which is why its diagnostic approach to regulated comparison categories tends to be well-tested, even for a first-time client in that exact space.

The same shortlist structure applies to any regulated B2B category — legal, healthcare, financial services — by swapping in the relevant compliance qualifier for the test prompt.

How to score the finalists before deciding

Once the shortlist is down to two or three finalists, score each one against the same weighted checklist used earlier, and add a final column for how each one's audit findings, if delivered, actually matched what you already suspected about your own citation gaps. Agreement there is a good sign of real diagnostic rigor rather than a generic template.

Also confirm, before signing, exactly how reporting will work going forward — the same fixed prompt set, tracked monthly, ideally tied to AI-referred conversions in your own CRM, since studies report AI referral traffic converting several times higher than average Google organic traffic.