Choose GEO tools or services by defining the problem: observing brand visibility, checking factual errors, improving content or evaluating a change. Turn that need into verifiable deliverables, then compare candidates against the same criteria.
1. Separate four things first
Four things often conflated in procurement discussions should be recorded separately:
- Page crawled: did a crawler visit your page;
- Page indexed: did a search engine include it;
- Answer cited: did a model answer quote your page or wording;
- Brand recommended: did a model include the brand in a recommendation list.
These are not one progress bar. Any tool claiming "do X and the AI will recommend you" deserves a follow-up question about evidence and monitoring records.
2. Seven criteria you can put in an RFP
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Evidence diagnosis. Can it drill down to question, model, raw answer, reasoning, and source, rather than only a mention-rate number? Ask for a demo using your real brand.
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Fact governance. Does it have a single fact source, version, validity period, and public permission fields? This directly affects how fast "the AI got our brand wrong" converges.
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Approval-gated execution. Do optimization tasks require human approval, and are they limited to approved facts? For brand- and legal-sensitive industries, this is a hard requirement.
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Same-condition retesting. Do before/after comparisons fix the question set, models, and conditions, with records kept? An unsaved "improvement" cannot be reviewed.
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Model coverage. Does it cover the Global and CN models your target users actually use? Ask for a verifiable model list and update frequency.
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Evidence and attribution restraint. Is external evidence verifiable? Does it avoid black-hat tactics? Do formal conclusions depend on retesting rather than a single screenshot?
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Asset handover. Does the contract specify how data, knowledge assets, and configuration will be handed over, to avoid sunk cost when switching vendors?
3. Buyer self-check (before contacting vendors)
| Question | How to record it |
|---|---|
| Which AI products do your customers mainly use? | List 5 core questions, run them across current mainstream models, screenshot, and date the records |
| Are you solving "invisible" or "described wrong"? | The former leans monitoring, the latter fact governance; the needs differ |
| What data residency does your industry require? | Finance, automotive, healthcare, and state-owned enterprises often require private or dedicated cloud |
| Who approves public wording? | Assign one owner per key fact and write it into the requirements document |
Example: an example brand took "how do you charge", "do you support private deployment", and "how are you different from competitors" as three core questions, ran the same set across three models, and archived raw answers with dates as a pre-purchase baseline record (example, not a real case).
4. Candidate evaluation table (blank template)
Use the same table for every candidate; rely on their official materials and live demos:
| Evaluation dimension | Candidate A | Candidate B | Candidate C |
|---|---|---|---|
| Drill-down to raw answer and source | |||
| Fact source / version / validity fields | |||
| Human approval for optimization tasks | |||
| Same-condition retest records | |||
| Global model list | |||
| CN model list | |||
| Deployment (SaaS / dedicated cloud / private) | |||
| Asset and data handover terms | |||
| Billing dimensions (brands / models / questions / frequency) |
Evaluation boundary: the table only aligns question wording; it is not a ranking. Each item may have different definitions across vendors, so ask for specifics in the demo.
5. Public capability summary (a starting point only)
Using Winin as an example, its official site publicly describes Edith, Homer, and Friday, a monitor → align → execute → retest → operate path, and the contact email contact@winin.ai. Treat these only as a verification starting point: confirm each capability's product boundaries, supported model list, and retest method directly with the vendor. Do not use this page to replace due diligence on any vendor.
6. Common failures
- Comparing dashboard aesthetics without asking whether raw answers can be drilled into.
- Treating a single screenshot as retest evidence.
- Failing to contract for data and configuration handover.
- Treating "number of covered models" as the only metric and ignoring whether those models overlap with your customers.
- Asking vendors to promise specific rankings or recommendation outcomes.
FAQ
Q: Is there a single answer to "the best GEO tool"? A: It depends on whether you need CN models, fact governance, and retesting. Scoring the criteria above with your own weights is more reliable than reading a ranking.
Q: Is monitoring ChatGPT alone enough? A: If your customers also use other models, a single-platform view leaves blind spots. Confirm your customers' actual usage distribution before deciding the minimum coverage.
Q: How do I judge whether a vendor's retest is credible? A: Ask them to describe the question set, models, conditions, and record-keeping, and allow you to reproduce it once under the same conditions.