From finding to fix, in one place.
Most AI visibility tools stop at the diagnosis. SurfaceGX closes the gap with a four-step loop that ends with a deployed, verified fix rather than a slide deck.
Scan: Read every surface
Before anything can be fixed, every machine-readable layer needs to be read. The scan step covers fetchability, crawler access, canonicals, robots.txt and sitemap configuration, discovery files (llms.txt), and structured data, across owned pages and third-party retrieval surfaces.
What runs
- Fetchability and render checks per URL
- Crawler access: GPTBot, ClaudeBot, OAI-SearchBot, Google-Extended
- Canonical, robots.txt, and sitemap integrity
- Discovery file presence (
llms.txt,ai.txt) - Structured data and schema coverage
Also running
- Classifies AI bots from your server logs
- Shows which priority pages GPTBot, ClaudeBot, and others actually requested
- Surfaces crawl gaps between what you expect and what engines fetch
Diagnose: Find the root cause
A score drop tells you nothing. Diagnosis separates retrieval failures (the engine never fetched the right page) from interpretation failures (it fetched and misread), then links each finding to a specific fix. This is where most tools stop.
What runs
- Asks each engine what it retrieved from your pages
- Separates retrieval failures from interpretation failures
- Identifies which assets visibility-critical pages depend on
- Pinpoints which engines can and can't reach your content
Also running
- Pressure-tests regulated claims against engine outputs
- Flags risky statements before they become citations
- Surfaces safer language with supporting evidence
Repair: Developer-ready artifacts
Every finding ships as something deployable: a Fix Card, a robots.txt change, a schema/JSON-LD patch, an llms.txt file, a content brief, or a GitHub pull request against your actual repo. Engineers merge it instead of translating it.
Interpretation gap fixes
- Writer-ready briefs for each interpretation failure
- Each brief cites the specific engine that misread the content
- Answer-first snippets tuned per surface (voice, chat, AI Overviews)
Infrastructure fixes
- Fix Cards with the exact code change required
- Brand manifests and crawler discovery files
- Schema/JSON-LD patches scoped to failing pages
- GitHub PRs opened against your repos for engineers to merge
Confirm: Re-scan to verify the fix landed
Shipping the fix is not the end. SurfaceGX re-runs the scan against the same pages after repairs are merged, confirming the finding is resolved and the score has moved. Repairs are kept accountable.
What runs
- Re-scan confirms deployed fixes resolved the finding
- Trends score movement over time
- Keeps repairs accountable with before/after evidence
- Packages results for stakeholder reporting
Then it starts again
- AI engines re-crawl on their own schedules
- New content means new surfaces to read
- SurfaceGX monitors continuously so fixes don't regress
The full repair stack
Six diagnostic engines and a reporting layer feed the loop. Most teams never think about them; they just merge the fixes. For the technical buyer who wants the depth, here's what's running.
Surface Audit
Scores fetchability, crawler access, canonicals, robots/sitemap, and discovery files across every owned and retrieval surface.
Engine Diagnosis
Asks each engine what it retrieved, separating retrieval failures from interpretation failures.
Content Repair
Turns interpretation gaps into writer-ready briefs, each citing the engine that misread you.
Engineering Handoff
Ships Fix Cards, manifests, and GitHub PRs against your repos. Engineers merge them instead of translating them.
Hallucination Risk Engine
Pressure-tests regulated claims, flags risky statements, and surfaces safer language with evidence.
Crawler Observatory
Classifies AI bots from your logs and shows which priority pages GPTBot, ClaudeBot, and others actually requested.
Progress & Reporting
Trends score movement, keeps repairs accountable, and packages results for stakeholders.
Want the deep version?
Definitions, evidence ledger, and the full methodology live in the docs.
Read the docs →See the loop run on your brand
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