This page is the full methodology: how we check four models against your verified facts, how errors get corrected at the source, and why every number in your dashboard is a count you can audit, not a score you have to trust.
Give us your domain. We build the set of buying questions people actually ask about your category, your products, and your competitors.
ChatGPT, Gemini, Perplexity, and Claude, asked with live web search on, the way real buyers use them. Every day, not once a quarter.
Nothing reaches you unverified. Your attention goes to real errors, not noise.
Our team corrects your pages, schema, product data, and the third-party sources models read. You approve everything before it moves.
Corrected answers get re-checked until they stick. You see the change, dated and logged, not a report saying we tried.
Monitoring is easy to do badly. These four pieces are what keep the output trustworthy.
Onboarding builds a verified fact sheet with you: prices, policies, availability, integrations, each with a source. Answers are judged against your documented facts, never against what an AI assumes is true.
Every collected answer gets one verdict: right, outdated, missing, or wrong, plus the verbatim quote that earned it. You never see a flag without the sentence that caused it.
Models answer with some randomness, so a verdict only changes after two consecutive scans agree. Your counts move when reality moves, not when a model has a jittery day.
Before any flag reaches your dashboard or your inbox, a person has looked at the answer, the quote, and the claimed violation. Machine scale, human judgment, in that order.
Most tools hand you a mystery number like 78 and call it a score. We don't, because nobody can say what a 78 means or why it beats a 74. Every headline number in Rightcited is a countable fraction of real answers.
"114 of 120 answers right." The denominator is how many answers we checked; the numerator is how many matched your truth file. When it moves, you can click through to the exact answers that changed.
"Recommended in 19 of 60 buying answers, next to your top competitor's 24." Not a share-of-voice index. A count of real recommendations, with the competitor's count sitting right beside yours.
Each flagged answer carries one estimate: how many buyers hear it monthly, from question volume times model usage. That's the only place weighting exists, and it's labeled as the estimate it is.
If a number can't be explained in one sentence with a numerator and a denominator, it doesn't go in your dashboard.
AI models move on their own schedule, so outcomes arrive on theirs. What your subscription buys is the work below, shipped every month and documented in your dashboard's work log, whether the models move fast or slow.
Your agreed question set checked across ChatGPT, Claude, Gemini, and Perplexity every day, with the live dashboard, change alerts, and a dated work log of every action we take.
Content, structured data, and product-data fixes on your own properties for every verified flag, shipped by our team with your access or handed to yours as ready-to-paste changes.
Written correction requests to the outside sources carrying verified errors about you, with follow-ups until resolution or documented refusal. Every thread logged.
A new fact page, FAQ, or Q&A asset each month, aimed at the question models keep answering wrong about you or not answering at all.
Setting the record straight in the forum and Reddit threads models actually cite, where platform rules allow it, honestly and without astroturf.
A direct line to the team working your brand with one-business-day response, plus the monthly proof report walking through what shipped and what moved.
Grow programs add citation placements, comparison-surface presence, and content volume on top, scoped in your agreement. And the line we hold everywhere: nobody can force a model to change its answer. We sell the inputs models respond to, execute them relentlessly, and count the results in the open.
The dashboard is the proof surface: your Citation Score with its denominator, what moved this week and why, model-by-model counts, the fix queue waiting on your approval, and a dated log of every scan. Open the demo below; it runs on a fictional brand with all the widgets live.
Demo dashboard · fictional brand, real interface. No signup, nothing to break.
Open the demo dashboardSwipe sideways to compare →
| Rightcited | Doing it yourself | A generic SEO retainer | Ignoring it | |
|---|---|---|---|---|
| Knows what AI tells your buyers | Every model, checked daily | Whenever you think to ask | Usually not tracked | No |
| Understands why models answer that way | It's the entire job | Guesswork | Occasionally | No |
| Gets errors corrected at the source | Done for you, every verified error | Nights and weekends | Broad scope, slow to aim here | No |
| Shows proof the answer changed | Re-checked until it sticks | More manual checking | Rarely in the report | No |
| Cost | $599/month, work included | Hours of your week | $2,000 to $10,000/month | Buyers, quietly, every month |
Or start smaller: the AI Page Check runs one page through all four models for $2.99.