The business is absent from the decision
In important buyer scenarios, AI systems do not mention the company or leave it out of the shortlist.
AI Visibility
AI visibility that does not stop at a score
We check whether your business appears, and how it is represented, in the AI scenarios that matter to buyers.
We separate observations and confirmed defects from hypotheses, find controllable changes and re-verify the result — without guarantees.
Request an AI visibility diagnosticWhen it is needed
When AI Visibility becomes a practical requirement
Three situations where AI-driven discovery shapes the choice and the cause of the visibility gap stays unclear.
The company is represented inaccurately
Services, specialization, location or differentiators are described incompletely, outdated or inconsistently.
Competitors are more visible, and the reason is unclear
You need to know where visibility differs, which sources appear alongside the answers, and what is actually controllable.
What Dazoro does
What the AI Visibility Diagnostic delivers
Buyer scenarios, evidence and controllability — without an opaque score or guarantees.
Buyer scenario map
We record the buyer scenarios that matter, the market/locale scope, competitors and the surfaces where discovery is observed.
Visibility and representation baseline
We check entity accuracy and observed sources, and code mention, recommendation and citation separately.
Findings and controllability
We separate observation, direct defect, supported hypothesis and actionable opportunity, and record ownership and the boundaries of control.
Action plan and verification
We prioritize actions with an owner, risk and rollback; a Sprint is proposed only for material, controllable findings.
Process
How the AI Visibility Diagnostic proceeds
We establish the buyer context
We define the services, market, locale, buyer stages and competitors, and freeze the scenario universe before the baseline.
We gather evidence
We use relevant first-party, vendor and manual observations, each with a source, timestamp, method and limitations.
We classify the findings
We separate observation, direct defect and supported hypothesis, then build the ownership/controllability map.
We form the action path
For material controllable findings, we define the owner, risk, rollback, a possible Sprint scope and the re-verification plan.
Boundaries
What is in scope, and what never becomes a promise
Included
- buyer scenario universe and evidence provenance
- AI discovery observations, entity accuracy and source landscape
- finding taxonomy and ownership/controllability map
- prioritized action plan and verification plan
Not promised
- guaranteed recommendations, rankings, traffic or revenue
- GEO/AI SEO hacks, prompt stuffing or "training the model on your brand"
- fake reviews, manufactured citations or link building by default
- strict causality from a single before/after observation
Approach
How the result remains verifiable
Every conclusion is tied to evidence, a finding class, ownership and a re-verification plan.
Evidence stays traceable
Observations keep their references, surface, timestamp, geo/locale, method and stated limitations.
An observation is not a cause
Direct defect, supported hypothesis and outcome signal remain separate classes of conclusion.
Change is re-verified
We repeat the compatible frozen core and report the observed change together with volatility and limitations.
Next step
Want to know where your business is visible — and what can actually change?
Describe your market, services and key buyer scenarios. We start with the Diagnostic: we fix scope, evidence and limitations before any promises.