Search any list of top ai visibility tools for enterprise seo teams and you'll find feature grids. Feature grids are how vendors want to be compared. They're rarely how your team should compare them.
This post takes a positioning-first approach: decide what job the tool is being hired to do, for whom, instead of what alternative, and then score tools against that. It includes a small, honest scoring script. The scenario is an illustrative private hospital group in Doha whose SEO team has been asked to report AI visibility to leadership.
Step 1: Define the job, not the features
Start with Kiyado's guide to measuring GEO. It sets out seven KPIs: brand mention rate, citation rate, competitor visibility, brand accuracy, citation quality, AI-referred traffic and conversions. A tool is only useful insofar as it helps you measure these reliably.
For a hospital group, two of those matter more than usual:
Brand accuracy. Healthcare information must be correct. If an AI assistant describes a department or service that a hospital doesn't offer, that's a patient-safety and trust issue, not just a marketing one. Any content changes made in response must be accurate and compliant with the healthcare advertising rules that apply in Qatar.
Citation quality. Patients and referring doctors should see the hospital described from authoritative, current sources.
So the job is: "Help us know, every month, whether AI describes our hospitals accurately for the questions patients ask, and who appears instead."
Step 2: Name the real alternative
If the team doesn't buy a tool, what does it do? Usually a fixed list of prompts, run by hand, recorded in a spreadsheet, as the guide describes: create a prompt set, record the baseline, repeat the test, connect to business data, find the gaps.
That manual baseline is your competitor. A tool has to beat it on the dimensions that matter to you, or it isn't worth the licence.
Step 3: Write your criteria as config
Keep evaluation criteria in version control, weighted by what matters for your job.
criteria.yaml : weights must sum to 100
job: "Monthly AI visibility and accuracy reporting for hospital services"
criteria:
fixed_prompt_sets: {weight: 15, question: "Can we define, version and freeze our own prompts?"}
mention_vs_citation: {weight: 15, question: "Are mentions and citations reported separately?"}
accuracy_workflow: {weight: 20, question: "Can clinicians or comms mark answers inaccurate, with notes?"}
source_visibility: {weight: 15, question: "Can we see and rate the cited sources?"}
competitor_tracking: {weight: 10, question: "Can we track named competitors per prompt?"}
raw_export: {weight: 10, question: "Can we export raw rows to join with traffic and enquiries?"}
collection_compliance: {weight: 10, question: "Is answer collection within each AI provider's terms?"}
arabic_and_english: {weight: 5, question: "Can prompts run in both Arabic and English?"}
Notice what's not there: a single "AI visibility score". The guide is explicit that GEO success can't be reduced to a single number.
Step 4: Score honestly
import yaml
def score(criteria_path, ratings):
"""ratings: {tool: {criterion: 0..3}}, where 0 = no, 1 = partly, 2 = yes, 3 = better than our manual baseline"""
crit = yaml.safe_load(open(criteria_path))["criteria"]
assert sum(c["weight"] for c in crit.values()) == 100, "weights must sum to 100"
results = {}
for tool, r in ratings.items():
missing = [k for k in crit if k not in r]
if missing:
raise ValueError(f"{tool}: unrated criteria {missing}")
results[tool] = round(sum(crit[k]["weight"] * r[k] / 3 for k in crit), 1)
return dict(sorted(results.items(), key=lambda x: -x[1]))
ratings = {
"manual_baseline": {k: 2 for k in ["fixed_prompt_sets", "mention_vs_citation", "accuracy_workflow",
"source_visibility", "competitor_tracking", "raw_export",
"collection_compliance", "arabic_and_english"]},
# "vendor_a": {...}, "vendor_b": {...} fill in after trials
}
print(score("criteria.yaml", ratings))
Rate the manual baseline too. If a vendor doesn't clearly beat it on your highest-weighted criteria, keep the spreadsheet a little longer.
Step 5: Trial with your own prompts
Never evaluate a tool on the vendor's demo prompts. Load your fixed prompt set and run both the tool and your manual process for the same month. Compare:
Do they agree on mentions and citations?
Does the tool surface accuracy issues your team found by hand?
Can you export the rows and join them to enquiries and appointment requests?
The guide's step four, connecting visibility to business data, is where many tools fall short. Test it explicitly.
Where audit tools fit
The guide mentions Geoptie and SEOmator, which assess things like citation readiness, schema, E-E-A-T and AI crawler access. These are diagnostic tools: they suggest why you're not cited. Monitoring tools tell you whether you are. Score them on separate criteria; they're hired for different jobs. Verify current capabilities before you rely on any of them.
Step 6: Segment the buyers inside the hospital group
"The SEO team" is rarely the only buyer of this data. In a hospital group, at least four groups care about different parts of it:
Leadership wants a monthly trend: are we appearing more for the questions patients ask, and are appointment requests following?
Clinical and medical affairs want accuracy alerts: is any AI answer describing a service, specialty or doctor incorrectly?
Communications want source quality: which outdated listings or articles are AI engines relying on?
The SEO team wants prompt-level detail: which prompts, which competitors, which cited pages.
Add a short criterion for each group to criteria.yaml, or score tools separately per audience. A tool that delights the SEO team but gives clinicians no way to flag errors isn't the right tool for a hospital, whatever its feature list says.
Step 7: Decide what "winning" means before the trial
Write down, before any trial starts, what result would make you buy. For example: "The tool must match our manual mention and citation results on at least most prompts, surface every accuracy issue our reviewers found, and export rows we can join to appointment requests." If you write this afterwards, it's too easy to talk yourself into the tool with the nicest charts.
Honest limits
AI answers vary between runs and by location. Any tool's numbers are estimates.
Collection methods matter. Prefer vendors with official API access or transparent, terms-compliant collection.
No tool replaces a human review of accuracy in healthcare.
Summary
Define the job. Name the real alternative. Weight what matters for your team. Trial on your own prompts. The best tool isn't the top of someone else's list. It's the one that does your job better than the spreadsheet does.
Kiyado works remotely with healthcare and enterprise teams in Doha to set up prompt sets and measurement before any tool is chosen.
See how Kiyado helps healthcare organisations with AI visibility →
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