When someone asks AI about your category, your brand is invisible, misread, or assembled from sources you don't control.

Traditional SEO tools and AI-monitoring dashboards were built to measure that decline. Measuring the output never fixes the input the engines actually read.

How it works

Where monitoring stops, repair begins

Every AI visibility tool can tell you something is wrong. The gap is the handoff: marketing sees a score drop, and no one can tell the developer what to change. SurfaceGX closes that gap with a root-cause diagnosis and the exact artifact a team deploys.

Your visibility dashboard

Reports the symptom

A prompt that should cite you doesn't.
Your visibility score dropped this week.
A page is flagged "low AI visibility."
An engine is summarizing you wrong.
The trend line, the alert, the export. Then it stops.

SurfaceGX

Names the cause and writes the fix

robots.txt blocks GPTBot, so the page never gets read.
Product schema conflicts with the page's canonical tag.
JSON-LD is missing on the pricing page, so models guess.
No llms.txt, so engines build your story from scraps.
Each finding ships as a Fix Card or a GitHub pull request. Then we re-scan to confirm it landed.

01 · scan

Read every surface

→

02 · diagnose

Root-cause diagnosis

→

03 · repair

Developer-ready artifact

→

04 · confirm

Re-scan to verify

Most tools stop after step 02.

Read across the surfaces that answer for you:

ChatGPT Claude Gemini Perplexity Google AI Overviews

Under the hood

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.

ScanM.01

Surface Audit

Scores fetchability, crawler access, canonicals, robots/sitemap, and discovery files across every owned and retrieval surface.

UnderstandM.02

Engine Diagnosis

Asks each engine what it retrieved, separating retrieval failures from interpretation failures.

FixM.03

Content Repair

Turns interpretation gaps into writer-ready briefs, each citing the engine that misread you.

ShipM.04

Engineering Handoff

Ships Fix Cards, manifests, and GitHub PRs against your repos. Engineers merge them instead of translating them.

ComplyM.05

Hallucination Risk Engine

Pressure-tests regulated claims, flags risky statements, and surfaces safer language with evidence.

ObserveM.06

Crawler Observatory

Classifies AI bots from your logs and shows which priority pages GPTBot, ClaudeBot, and others actually requested.

TrackM.07

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 →

Built by operators

The people who lived the problem

SurfaceGX was founded by operators who watched AI distort brand narratives in ways no traditional measurement tool could catch. It is where communications strategy meets data infrastructure.

Co-Founder, AI Visibility Strategy

Jeanine Morgan

20 years building PR and earned-media programs for reputation-sensitive brands. She co-founded SurfaceGX after seeing AI rewrite brand narratives no comms tool could detect.

Co-founder & CPO, Product Engineering

John Canneto

Product architecture behind the audit, diagnostic, and repair workflows. Background across AI, data infrastructure, and enterprise access layers.

Enterprise and regulated work is founder-led. See self-service plans →

See why AI gets your brand wrong.
Then ship the fix.