How to Evaluate a GEO Solution
The best GEO solution for a B2B product is the one that measures honestly, understands your vertical, and works on your product layer as well as your content. Most of the market fails at least one of those three tests.
A disclosure before the checklist: I run GrackerAI, a GEO platform for B2B SaaS and cybersecurity, so I am a vendor in this market. I have written these criteria so they apply to my own company as strictly as to anyone else's, and I would rather you use them to reject us than buy anything without them.
Three kinds of help
You will choose among three models, often in combination.
| Model | Good for | Watch for |
|---|---|---|
| Visibility platform | Repeated sampling across engines, monitoring, reporting | Dashboards that hide variance; generic prompt sets |
| Agency or consultancy | Content, authority programs, audits, extra hands | Paid by volume, so every gap becomes more content |
| In-house practice | Prompt set, truth sheet, fact model, product-layer fixes | Slow start without tools or outside experience |
Chapter 10's rule applies: buy the commodity, own the product. Platforms and agencies can supply measurement and capacity. The truth sheet, prompt set, fact model and product-layer decisions stay with you.
Criterion 1: Does it measure honestly?
Because AI answers vary run to run and engine to engine (Chapter 3), measurement honesty is the first filter.
Ask how many times each prompt is sampled per engine, and how the tool reports variance. Ask which engines it covers and whether it queries them the way buyers use them, including logged-out web interfaces where that matters, rather than only through APIs that may behave differently. Ask whether you can see the raw answers behind every score.
A good solution shows you distributions and lets you inspect the underlying answers. A weak one gives you a single score that moves every week for reasons nobody can explain.
Criterion 2: Does it measure accuracy, not just presence?
Most tools tell you whether you were mentioned. Few tell you whether what the engine said was true.
Ask whether the solution can score answers against your own truth sheet, flag wrong and vague descriptions, and track accuracy over time. For a product-resident approach, description accuracy is the metric that tells product teams what to fix.
Criterion 3: Does it understand your vertical?
Ask how the prompt set is built. A generic set seeded from keyword tools will miss the compliance-specific, integration-specific questions that create pipeline in B2B (Chapter 2). The better answer involves your sales calls, RFPs and support tickets, and people who know your domain's vocabulary.
Ask which authority sources matter in your vertical. If the answer is a generic list (Reddit, Wikipedia, YouTube), the solution is horizontal. In security, you should hear about standards bodies, peer review platforms, advisories and the environments your buyers research in.
Criterion 4: Does it touch the product layer?
This is the criterion most solutions fail. Ask what the audit covers. If it covers only content extractability, schema and technical crawlability, it covers three layers out of four.
A product-aware solution examines feature naming against buyer language, documentation coverage and accessibility, product fact consistency across owned and third-party surfaces, and capability exposure. It produces findings product teams can act on, written as product tickets with evidence, not as a list of articles to write.
Criterion 5: Does it support experiments?
Ask whether you can annotate changes, define control and treatment prompt groups, and compare them over time. Ask how the solution separates your changes from model updates that move everyone. If the answer is "you'll see the score go up," keep looking.
Criterion 6: What does it claim?
Claims reveal method. Be cautious when you hear:
- Guaranteed citations or rankings. No one controls the engines.
- A single tactic as the lever. llms.txt, schema markup or a content format sold as the thing that gets you cited. The 2026 evidence on these is weak (Chapters 7 and 8).
- An "AI rank." Answers do not have stable positions. A rank implies a stability that does not exist.
- Results in days for training-dependent changes. Retrieval can respond quickly. Changes that depend on what models learned in training take far longer.
Good vendors talk about probabilities, time windows and what they do not know.
Criterion 7: Who owns the data?
Ask whether you can export your prompts, raw answers, scores and history in a usable format. Your measurement history is the evidence base for every experiment you run. If it cannot leave with you, you are renting your own learning.
Criterion 8: How are incentives aligned?
Ask how the solution is paid. An agency paid per article will tend to recommend articles. A platform priced by prompt volume may push you to track more prompts than you can act on. Neither is wrong, but you should know which way the incentive leans, and favor arrangements tied to outcomes you defined, such as description accuracy on your pipeline prompts.
The evaluation scorecard
Use these questions in every vendor conversation.
| Question | A good answer | A red flag |
|---|---|---|
| How many runs per prompt per engine, and how do you show variance? | A specific sampling design and visible distributions | "We check daily" with one score |
| Can you score accuracy against our truth sheet? | Yes, with wrong and vague flags | Mentions only |
| How do you build our prompt set? | From our deals, tickets and domain experts | From keyword tools alone |
| Which sources matter in our vertical? | A domain-specific map | A generic list |
| Does your audit cover naming, docs, product data and exposure? | Yes, with product-ready findings | Content and schema only |
| How do you separate our changes from model updates? | Controls and annotations | "The score will show it" |
| What results do you guarantee? | None; here is how we measure | Citations or rankings |
| Can we export everything? | Yes, prompts, answers and history | Partial or no export |
Score each vendor, including any you already use. Then decide which parts to buy, which to hire for and which to keep in-house.
The question for this chapter: which of these eight questions would your current GEO vendor struggle to answer?
Key takeaways
- Choose among visibility platforms, agencies and an in-house practice, usually in combination.
- Judge every option on eight criteria, starting with honest measurement and accuracy scoring rather than mentions alone.
- Ask whether the audit covers the product layer: naming, docs, product data and exposure.
- Be wary of guaranteed citations, single-tactic levers, "AI ranks" and fast results for training-dependent changes.