Observe
Run approved prompts repeatedly through configured OpenAI, Gemini, Perplexity, and Claude APIs. Preserve the exact prompt, model, methodology, locale, source metadata, and outcome.
THE CITESTRA PLATFORM
Answers are only the beginning. Evidence tells you what to do next.
Measure AI visibility across four provider APIs, understand the source and site evidence behind a gap, and make focused changes you can evaluate over time.
APPROVED PROMPT
Provider observation
Evidence record
Diagnostic case + Fix
Comparable follow-up
Each step retains its source and methodology.ONE CONNECTED WORKFLOW
Know what was seen, why it matters, and what to check next.
Every step links back to the evidence available at the time. Product decisions remain yours.
Diagnose
Build bounded diagnostic cases from successful responses and public crawl findings. Keep observed facts, inferences, and hypotheses separate.
Improve
Review a targeted Fix with its evidence, confidence, implementation steps, editable starting artifact, and metric to watch.
Validate
Compare repeated cohorts after implementation while respecting prompt, model, provider, and methodology boundaries.
EVIDENCE YOU CAN INSPECT
Follow a conclusion back to its source.
Citestra’s diagnostic record is more than a headline. It connects repeated response counts, provider coverage, a public page target, dated evidence, and a versioned explanation. A person can review the case before acting.
The answer that was actually returned
Successful responses contribute to rates. Failed and skipped requests appear in completeness, not as brand absence.
Where a provider cited or grounded its answer
Provider-native citations are distinguished from grounding/search sources and URLs appearing only in prose.
What is visible on your public pages
The crawl can identify access rules, indexing directives, category clarity, and whether a suitable target page exists.
How a case becomes a proposed action
Diagnostic cases retain evidence IDs and separate facts, inferences, hypotheses, and confidence. Thin evidence may yield no Fix.
Insufficient evidence is a valid outcome.
If the observation set does not justify a specific diagnosis, Citestra records that status and does not create a precise Fix.
FROM EVIDENCE TO A CHANGE
A Fix your team can review and implement.
The target, supporting claims, proposed action, and measurement plan are presented together.
ILLUSTRATIVE FIX
Clarify the product category on a public page
A diagnostic case shows that approved category prompts repeatedly omit the brand, while the crawled page introduction does not name the product category clearly.
Proposed change
Update the page introduction with verified product language that states who the product serves and what category it belongs to.
Target page and affected prompt cluster
Evidence links, source dates, and confidence
Facts separate from hypotheses
Editable starting artifact; no auto-publishing
Metric to watch after implementation
MEASUREMENT YOU CAN TRUST
Provider differences stay visible.
OpenAI, Gemini, Perplexity, and Claude can return different answers to the same question. Citestra keeps their sample sizes, source semantics, and methodology identities intact, including when models change.
See the methodologyRepeated observationsOne prompt × provider × model × locale × repetition
Honest citation rulesNative citation evidence is distinct from an inline URL
Partial scan visibilityFailed and skipped work is visible and excluded from rates
Comparable validationPrompt and methodology changes create boundaries
QUESTIONS WE HEAR
Clear answers about the data.
Do you measure consumer ChatGPT, Gemini, Perplexity, or Claude?
Citestra measures configured provider APIs. Those responses can differ from the consumer applications because surfaces, personalization, tools, and configurations differ.
Does a citation mean a URL appeared in the answer?
No. Canonical Citation Rate uses provider-native citation evidence linked to a confirmed first-party domain. Plain generated URLs do not count.
Can a Fix guarantee more AI citations?
No. A Fix is an evidence-backed proposed intervention. After implementation, Change Sets compare compatible repeated observations; a change in rate is not proof of causation.
What if a provider request fails?
The failure remains visible in scan completeness. It is excluded from mention, recommendation, and citation rate denominators.
Can I see which provider disagrees?
Yes. The workspace preserves per-provider sample sizes and rates alongside aggregate metrics.
START WITH AN OBSERVATION
Start a free audit Turn AI visibility into a decision you can defend.
See what your site says, which prompts matter, and where evidence suggests a change.