Currents methodology
A visibility number is only worth trusting if the questions are fair, the panel is consistent, and the evidence is kept. Here is the discipline behind every number in your report.
Every Currents benchmark looks at your place through three lenses. Blind discovery asks the questions travelers ask before they know you exist, and your name appears nowhere in them. Named questions ask what AI tells travelers who are already looking at you. Peer questions ask where AI places you when travelers weigh their options.
Each lens has one job, and only blind discovery moves Answer Share. Your score can’t be flattered.
Your agents don’t run a generic question list. They build a working model of the place you market: the audiences you draw, the seasons that matter, the trips people plan, the markets they come from. The benchmark is generated from that Place Model, which is why two hotels on the same street get two different benchmarks. And the model isn’t hidden: it sits right in your report, so you always know what your benchmark is built on.
A traveler deciding from Seattle plans differently than one deciding from Dallas. Currents asks the way travelers from your feeder markets actually ask, so your Answer Share reflects the audiences you actually draw.
Generated for your place
“Where should travelers from Seattle go in Southern California for a food-and-culture long weekend?”
One of your blind discovery questions. Your name is nowhere in it. That’s the point.
The same questions run across ChatGPT, Claude, Gemini, and Perplexity. That’s hundreds of real answers every run, all of them kept. And because AI answers move, your agents keep running, week after week, so you see trends instead of snapshots.
Same questions · Every engine · Week after week
Currents checks what AI leans on: your official pages, partners, media, and third-party sites, grounded in live search where it matters. That’s Authority Share: which voices are shaping your answer, and whether yours is one of them.
Every question keeps its receipt: the question asked, the engine that answered, when it answered, what it said, who got mentioned, and what it cited. Every figure in your report opens back to real answers. No black box.
Question
“Where should travelers from Seattle go in Southern California for a food-and-culture long weekend?”
Engine
Perplexity
Observed
This week’s run
Mentioned
Yes
Answer excerpt
“...for a coastal long weekend, La Jolla stands out: walkable coves, tide pools, and standout dining in a small village footprint...”
Citations
lajolla.travelafar.com
Every figure opens back to answers like this
Every claim in the technical audit traces to something we observed on your site. Here is each check, what it can prove, and what it cannot.
What we observe
We fetch your robots.txt and read the published rules for each AI crawler we track.
What it proves
Whether your published rules allow or disallow those crawlers.
What it cannot prove
Whether a provider actually visits your site, or when.
What we observe
We request /sitemap.xml, follow any sitemap your robots.txt declares, and confirm the winning response is an XML sitemap.
What it proves
A sitemap is published and readable at a discoverable location.
What it cannot prove
Whether search or AI systems have fetched it.
What we observe
We read the canonical link tag from your homepage HTML.
What it proves
The tag is present or missing.
What it cannot prove
How crawlers resolve duplicate URLs across the rest of your site.
Homepage reachable: every completed audit first verified that your homepage responded with a real page instead of an error or a security wall. This is a precondition, not a scored check.
What we observe
We read the title tag from your homepage HTML.
What it proves
Presence and the exact text served.
What it cannot prove
Which title an engine chooses to display.
What we observe
We read the meta description from your homepage HTML.
What it proves
Presence and the exact text served.
What it cannot prove
Which snippet an engine chooses to display.
What we observe
We read the lang attribute on your html element.
What it proves
The declared language.
What it cannot prove
How well systems handle your content's language.
What we observe
We read Open Graph and Twitter card tags from your homepage HTML.
What it proves
Presence and completeness of social metadata.
What it cannot prove
How each platform renders your preview.
What we observe
We request /llms.txt.
What it proves
Whether the file is published.
What it cannot prove
Whether any AI provider reads it. Major providers have not confirmed doing so.
What we observe
We parse JSON-LD from your homepage HTML, plus rendered evidence when available.
What it proves
Structured data exists and which types it declares.
What it cannot prove
That any engine uses it in answers.
What we observe
We compare declared types against place and travel types.
What it proves
Whether types relevant to your entity are present.
What it cannot prove
That relevant types improve any specific answer.
What we observe
We count H1 and H2 headings in your homepage HTML.
What it proves
The heading outline as served.
What it cannot prove
Whether the writing under those headings is good.
What we observe
We measure the visible copy on your homepage as it reads before JavaScript runs, with code and styling removed.
What it proves
How much readable text is served up front, which is how most AI systems read pages.
What it cannot prove
Content quality, or what appears after JavaScript runs.
What we observe
We measure sentence length of that same served copy.
What it proves
Average sentence length of the served copy.
What it cannot prove
Whether the writing is clear to a human reader.
What we observe
We check the alt attribute on homepage images. An empty alt counts as an intentional choice for decorative images.
What it proves
Presence of alt attributes.
What it cannot prove
Whether the descriptions are accurate.
What we observe
We count links from your homepage to your own site.
What it proves
How many same-site paths the homepage offers.
What it cannot prove
Your full site architecture.
What we observe
We request Google Lighthouse lab data through the PageSpeed Insights API.
What it proves
Google's lab measurement at audit time.
What it cannot prove
Real visitor experience. When Google returns no data, the check reads unknown and is not scored.
What we observe
We request Google Lighthouse lab data through the PageSpeed Insights API.
What it proves
Google's lab measurement at audit time.
What it cannot prove
Real visitor experience. When Google returns no data, the check reads unknown and is not scored.
Provider statuses on the crawler matrix read your published robots.txt rules. Allowed means your published rules do not disallow that crawler. It is a policy observation, not a visit log.
Your agents read your website the way AI reads it: your homepage, your key pages, your sitemap. Every gap in your report is a specific place where AI needed support and your official story went quiet. Not generic advice.
Your pages → your gaps → your fix
A score tells you where you stand. It doesn’t write the fix. Currents turns every gap into an action with a draft already started, written in your voice, because your agents learned it from your own site. You review, you publish, you take the credit. Nothing ships without you, and nothing starts from a blank page.
Drafted for you · Published by you
When you ship a fix, it doesn’t disappear into a dashboard. Completed work sits beside the answers that follow, so you can show your board the trail: what was asked, what changed, what AI says now.
See where AI includes you, where official support is thin, and the first fix ready in your dashboard.