BACKTIER
ServicesAgentic Visibility Infrastructure

Visibility Audit Systems

An audit should not just describe what is wrong. It should create the operating instructions for how to become more legible, more trusted, and more likely to be included in high-value answers.

5+
AI engines evaluated per audit
8
Audit output dimensions
6
Audit types available
Recurring
Monitoring cadence option

BackTier builds audit systems that repeatedly evaluate how an organization is understood across AI systems, search engines, websites, media references, public records, third-party databases, and competitor comparison surfaces. These systems turn visibility into an operating discipline instead of a one-time report.

01

AI Visibility Snapshot

The audit begins with a structured evaluation of how the organization appears across major AI systems: ChatGPT, Gemini, Perplexity, Google AI Overviews, and other answer engines. The snapshot captures what AI systems say about the organization, how they classify it, what competitors or peer organizations they associate with the category, and what they omit.

The AI visibility snapshot is the most important output of the audit because it reflects the actual information environment that buyers, voters, donors, journalists, and partners encounter when they ask AI systems about the organization's category.

02

Entity Clarity Review

Entity clarity is the degree to which AI systems and search engines can confidently identify, classify, and describe an organization. Organizations with weak entity clarity are frequently misclassified, omitted from relevant answers, or described with inaccurate or outdated information.

BackTier's entity clarity review evaluates the organization's structured data, schema markup, Wikipedia and Wikidata presence, knowledge panel status, third-party citations, and the consistency of information across all public sources.

03

Search and Answer-Engine Review

The search and answer-engine review evaluates how the organization appears in traditional search results, featured snippets, knowledge panels, local packs, and AI-generated answers. It identifies which queries the organization ranks for, which it should rank for but does not, and which competitors are capturing those positions.

The review also evaluates the organization's answer-engine optimization — the degree to which its content is structured to be cited as a direct answer to questions that buyers, voters, or partners are asking.

04

Narrative and Misclassification Review

AI systems sometimes describe organizations inaccurately — using outdated information, conflating them with similarly named entities, or associating them with incorrect categories. The narrative and misclassification review identifies these problems and documents the specific sources that are contributing to the inaccurate description.

For organizations in regulated industries, high-trust categories, or politically sensitive positions, narrative accuracy is not just a marketing concern — it is an operational and reputational risk.

05

Competitor Comparison

The competitor comparison evaluates how the organization's AI and search visibility compares to its primary competitors. It identifies where competitors have stronger entity signals, better-structured content, more third-party citations, or more favorable AI descriptions.

The comparison is not used to copy competitors — it is used to identify the specific gaps that are causing the organization to lose visibility to less capable but better-structured competitors.

06

Source Gap Analysis

The source gap analysis identifies the specific public sources that are missing, weak, or inaccurate for the organization. These sources include the organization's own website, Wikipedia and Wikidata entries, industry directories, news coverage, academic citations, government records, and third-party review platforms.

Each missing or weak source is prioritized by its likely impact on AI visibility and entity clarity. The analysis produces a prioritized list of source gaps to close.

07

Public Information Accuracy

Public information accuracy evaluates whether the information that AI systems and search engines have about the organization is correct, current, and consistent. Inaccurate public information — wrong addresses, outdated leadership, incorrect service descriptions, conflated entities — reduces AI visibility and erodes trust.

BackTier identifies every inaccuracy and provides specific correction instructions for each source.

08

Priority Action Plan

Every audit concludes with a priority action plan that specifies what to fix first, what to fix second, and what to monitor on an ongoing basis. The action plan is organized by impact and effort — quick wins are separated from structural changes that require more time.

The action plan is formatted for operational use. It is not a strategy document — it is a work order.

Measurable Outcomes

Complete picture of how AI systems describe the organization today
Entity clarity score with specific improvement instructions
Competitor comparison with gap analysis
Source gap analysis with prioritized correction list
Public information accuracy review with correction instructions
Priority action plan organized by impact and effort
Recurring monitoring option to track improvement over time

Our Process

01

Snapshot

Evaluate how the organization appears across all major AI systems.

02

Entity Review

Assess entity clarity, schema, and third-party citation quality.

03

Search Review

Evaluate search and answer-engine positioning.

04

Narrative Review

Identify misclassifications and inaccurate descriptions.

05

Gap Analysis

Map every missing or weak public source.

06

Action Plan

Deliver a prioritized work order for improvement.

Common Questions

Ready to get started?

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We'll analyze your brand's current AI citation rate across ChatGPT, Perplexity, Gemini, Claude, and Grok — then show you exactly what it takes to dominate AI search in your category.

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