How many times do you run each prompt?

Every tracked prompt runs multiple times per scan across each provider. We aggregate the runs into a stable signal so a single bad sample doesn't flip your score. Daily polling means weekly trends, not one-shot snapshots.

Why four providers instead of one?

Buyers don't use one model. ChatGPT, Claude, Gemini, and Perplexity each weight different sources, surface different competitors, and answer with different confidence. Tracking one provider gives you a partial truth.

What's the difference between a mention and a citation?

A mention is your brand named in the answer text. A citation is your domain linked in the source list. Both matter, but citations move buyers further down the funnel. We separate them so you know which gap to close.

How do you frame personas?

Each prompt is scanned with a buyer persona attached — role, stage, intent. The same question from a VP of Engineering and a procurement lead returns different answers, and we track both separately so your scoring matches your actual ICP.

How is opportunity score calculated?

Each opportunity gets three sub-scores: impact (how much it would move your visibility), confidence (how consistently the gap appears across runs and providers), and effort (how hard it is to ship). Priority is the combined score.

What does draft generation actually produce?

A complete first draft of the asset the opportunity calls for — a page outline, a pitch email, a community post, or a fact sheet. Written in your buyer's voice using the gap evidence as input. You edit and ship.

What's playbook learning?

Every time you ship an opportunity, we track whether your visibility moved on the prompts tied to it. Over time the system learns which asset types work for your category and weights future recommendations accordingly.

Do you store the raw model responses?

Yes. Every run is stored so any score can be audited back to the source. If you want to see the actual ChatGPT answer that drove a finding, it's one click away.

Methodology

How Monroya works.

The full methodology — from scanning to scoring to the asset draft. No black box.

Updated: June 2026

Multi-run scanning

A single prompt to ChatGPT can return different answers minute to minute. To make a score that means something, we run every tracked prompt multiple times per scan, on a daily cadence, and aggregate the runs into a stable signal. Outliers get smoothed; persistent patterns get surfaced. Your daily score reflects what AI consistently says, not what it said once.

Four providers, not one

Monroya scans ChatGPT (GPT-5 and 4-class), Claude, Gemini, and Perplexity in parallel. Each provider weights sources differently: Perplexity leans on cited domains; ChatGPT favors well-known brands; Gemini pulls from Google's index; Claude tends toward documentation. Looking at all four together is the only way to know whether a gap is universal or model-specific — and the fix differs.

Persona framing

A VP of Engineering asking "best observability tools" gets a different answer than a procurement lead asking the same question. Every prompt in your account is scanned with a buyer persona attached — role, funnel stage, intent. We track your visibility per persona so you see where you're strong with technical evaluators but invisible to economic buyers, or the reverse.

Mention vs citation

A mention is your brand named in the answer text. A citation is your domain linked in the source list. They behave differently: mentions build awareness, citations drive clicks. Most tools collapse them into one number. We track both, because the fix for a mention gap is brand presence on third-party sources, and the fix for a citation gap is your own pages getting indexed and cited.

Opportunity scoring

Every gap we find becomes an opportunity with three sub-scores: impact (how much closing it would move your visibility), confidence (how consistently it shows across runs and providers), and effort (how much work the fix is). Priority is the combined score. We show you the math, not just the rank — so you can argue with it.

Draft generation

When an opportunity needs a new asset — a comparison page, a pitch to a publication, a Reddit answer, a fact sheet — clicking Execute drafts it. The draft uses the gap evidence as input and writes in your buyer's voice, not generic marketing copy. You edit and ship. No copywriter handoff, no two-week loop.

Playbook learning

Every time you ship an opportunity, we watch whether your score moves on the prompts tied to it. Over weeks, the system learns which asset types work for your specific category — comparison pages might move B2B SaaS scores, while community posts might move dev tools. Future recommendations get weighted by what actually worked, not by generic best practice.

Auditable by design

Every score traces back to the raw model responses that produced it. If your AI Share of Voice dropped three points, you can see which prompts moved, which providers shifted, and what the actual answer text was. Nothing about the methodology is hidden behind a black-box score.

Frequently asked questions

How many times do you run each prompt?: Every tracked prompt runs multiple times per scan across each provider. We aggregate the runs into a stable signal so a single bad sample doesn't flip your score. Daily polling means weekly trends, not one-shot snapshots.
Why four providers instead of one?: Buyers don't use one model. ChatGPT, Claude, Gemini, and Perplexity each weight different sources, surface different competitors, and answer with different confidence. Tracking one provider gives you a partial truth.
What's the difference between a mention and a citation?: A mention is your brand named in the answer text. A citation is your domain linked in the source list. Both matter, but citations move buyers further down the funnel. We separate them so you know which gap to close.
How do you frame personas?: Each prompt is scanned with a buyer persona attached — role, stage, intent. The same question from a VP of Engineering and a procurement lead returns different answers, and we track both separately so your scoring matches your actual ICP.
How is opportunity score calculated?: Each opportunity gets three sub-scores: impact (how much it would move your visibility), confidence (how consistently the gap appears across runs and providers), and effort (how hard it is to ship). Priority is the combined score.
What does draft generation actually produce?: A complete first draft of the asset the opportunity calls for — a page outline, a pitch email, a community post, or a fact sheet. Written in your buyer's voice using the gap evidence as input. You edit and ship.
What's playbook learning?: Every time you ship an opportunity, we track whether your visibility moved on the prompts tied to it. Over time the system learns which asset types work for your category and weights future recommendations accordingly.
Do you store the raw model responses?: Yes. Every run is stored so any score can be audited back to the source. If you want to see the actual ChatGPT answer that drove a finding, it's one click away.