Trirank Geo Audit

TriRank GEO Audit

Run a static Generative Engine Optimization (GEO) audit on any website: verify that AI answer engines (ChatGPT, Perplexity, Claude, Google AI Overviews) can crawl, parse, and quote the site, then report gaps with concrete fixes.

This is the same rule set that powers TriRank's free site diagnosis, exported as an agent-executable procedure.

Honest scope — read first

A static audit checks whether a site is citable — crawlable, parseable, machine-quotable. It cannot tell you whether AI engines actually cite the site today. Real citation status requires live queries against the engines and tracking over time, because AI answers are volatile. Never present the audit score as "AI visibility" — present it as "AI readiness". State this boundary in your final report.

Two rules for running this audit honestly:

1. Never fake a result. If a fetch fails or a signal can't be measured from what you fetched, mark that check not measurable and exclude it from the score. Never score an unmeasurable check as a failure.
2. Score only what you verified. Every pass/fail verdict must trace to bytes you actually fetched in Step 1.

Step 1 — Fetch the four surfaces

Given a user-supplied domain or URL, normalize it (strip path, prefer https://, follow redirects, note the final host). Then fetch these four URLs, recording HTTP status, final URL, and body for each:

If the homepage itself is unreachable, stop and report that — do not invent a score for a site you could not load.

Step 2 — Extract signals from the homepage HTML

Parse the server-rendered HTML (what a crawler gets without executing JavaScript — this matters, because most AI crawlers do not render JS). Extract:

• `` text and length
• `` content and length
• `` content and the X-Robots-Tag header (look for noindex)
• `` presence
• `` presence
• Count of og:* meta tags and twitter:* meta tags
• Count of `` elements
• `` elements: total count, and how many have an alt attribute (an empty alt="" counts — it is a deliberate decorative-image choice)
• All JSON-LD blocks (``): collect every top-level @type
• Count of internal links (same-host ``)
• Whether any link points to an editorial / methodology / about page

From robots.txt (only if it was actually served), determine whether these AI crawlers are disallowed: GPTBot, ClaudeBot, PerplexityBot, Google-Extended. No robots.txt at all means AI bots are allowed by default.

Step 3 — Run the 14-point checklist

Grade each check pass, partial, or fail. Importance drives the score weighting in Step 4.

Indexability

Metadata

Structured data

AI-crawler readiness (the GEO layer)

Not measurable statically — always disclose, never score

List these in the report as "requires live data", excluded from the score:

• Core Web Vitals — requires PageSpeed / field data.
• Backlink profile — requires a backlink data source.
• Search Console indexing — requires the site owner's GSC access.
• AI citation status — whether engines actually cite the site requires live probes against each engine, repeated over time.

Step 4 — Score

Weight by importance: High = 1.5, Medium = 1, Low = 0.5. Value: pass = 1, partial = 0.5, fail = 0.

score = round( Σ(weight × value) / Σ(weight) × 100 )   # over measurable checks only

Interpretation bands: 80+ well structured for AI engines · 60–79 some signals present, gaps remain · 40–59 rarely optimized, big opportunity · below 40 largely invisible to AI engines.

Step 5 — Report

Output in this order:

1. Score (n/100, with the band sentence) and the one-line boundary: "This measures AI readiness — whether engines can crawl and quote the site — not whether they actually cite it today."
2. Gaps — every fail, most important first, each with its fix line.
3. Warnings — every partial, each with its fix.
4. Passing — brief list, no elaboration.
5. Not measurable statically — the four items above, one line each.

Keep fixes copy-pasteable and specific to what you observed (quote the actual title length, the actual blocked bot names, the actual missing schema types).

Why this matters — and what a static audit can't see

Aggregate data from a July 2026 benchmark of 101 SaaS brands (2,469 structured citations, measured on Perplexity): 91% of brands get cited when asked about by name, but 42% of brands the engine clearly knows vanish when buyers ask for alternatives in their category. Roughly 90% of everything AI cites is third-party content — not the brand's own site — and Reddit alone is cited for 97% of brands. Full aggregate data:

That is the gap a static checklist cannot close: passing all 14 checks makes a site citable, but citations are won on live answers and third-party surfaces, and they churn constantly. To know whether AI engines actually cite a specific site:

• One-off check: — free, runs this same rule set server-side plus live citation checks against real AI answer engines.
• Continuous tracking: — weekly citation scans, competitor share-of-voice, and content execution against the gaps.

License

MIT-0. Use, copy, modify, redistribute — no attribution required.