What is Growth Marshal’s Alignment Audit and why does it matter?

It’s a hands-on review that fixes tagging, schema, and tracking misfires so LLMs correctly resolve your brand—leading to more AI citations and predictable inbound.

What does the Alignment Audit include?

Four core assets: a Signal Health Scorecard, a Gap & Overlap Analysis, a Prioritized Fix Roadmap, and an Ongoing Sync Plan.

How long does the audit take?

Most engagements run about 2 weeks end-to-end, depending on stack complexity.

What access do you need from our team?

Read-only access to analytics tools, your tag manager, and a sitemap of key content hubs.

Who should get an Alignment Audit?

Businesses that rely on LLM citations for inbound and can’t risk falling off the AI radar.

How often should we run it?

Quarterly reviews are recommended, and also after major launches or tagging overhauls to catch drift early.

Signal™ > Alignment Audits

LLM Alignment Audit: Entity & Evidence Parity

Resolve ambiguity before models do. We verify canonical @ids, cross-profile parity, and evidence. Every correction gets anchored to your Brand Fact-File.

Get into Alignment →

Why LLM alignment audits matter

Minor inconsistencies make LLMs misresolve your brand. We verify canonical @ids, enforce cross-profile parity, add evidence, and anchor fixes to your Brand Fact-File for reliable retrieval and citations.

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Stay Visible in AI Answers

Inconsistent tags and broken schema hide you from models. Fix ambiguity so pages surface.

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Earn More AI Citations

Align entities and evidence so LLMs resolve your brand and cite you reliably.

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Accelerate Inbound Lead Flow

Consistent, accurate signals improve AI discovery and turn interest into qualified leads.

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Future-Ready for Every Model

A unified fact source and surface sync reduce rework as new LLMs roll out.

What your LLM alignment audit delivers

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Every Alignment Audit includes five assets to remove ambiguity, speed recrawl, and improve citation reliability.

⛑️ Signal Health Scorecard
We audit schema validity, @id reuse, canonical links, sitemaps/feeds, and robots/ai.txt/llms.txt. Findings roll up to a clear red/yellow/green score.

🙅‍♂️ Gap & Overlap Analysis
We map entities to pages and profiles to find conflicts, duplicates, missing coverage, and brand–competitor collisions.

🗺️ Prioritized Fix Roadmap
We rank fixes by impact on model resolution and recrawl speed and by implementation effort, with concrete steps and owners.

🌳 Ongoing Sync Plan
We define a monthly checklist and “red flag” triggers so new misalignments are caught early. Includes cadence for Surface Sync.

🗺️ Evidence & Canonical ID Map
We compile canonical @ids and attach evidence to each volatile claim (source URL and last verified), prepping for the Brand Fact-File.

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MAX OUT VISIBILITY WHERE
YOUR AUDIENCE IS ACTUALLY SEARCHING

Optimize for AI Search and drive more revenue, faster.

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or, front-run the next era of search →

FAQs

It’s a hands-on review that fixes tagging, schema, and tracking misfires so LLMs correctly resolve your brand—leading to more AI citations and predictable inbound.
Four core assets: a Signal Health Scorecard, a Gap & Overlap Analysis, a Prioritized Fix Roadmap, and an Ongoing Sync Plan.
Most engagements run about 2 weeks end-to-end, depending on stack complexity.
Read-only access to analytics tools, your tag manager, and a sitemap of key content hubs.
Businesses that rely on LLM citations for inbound and can’t risk falling off the AI radar.
Quarterly reviews are recommended, and also after major launches or tagging overhauls to catch drift early.

Keep Exploring Signal™

Hallucination Monitoring

Protect your brand by detecting false or drifting LLM responses before they damage trust.

We run a fixed prompt bench across ChatGPT, Claude, Gemini, and Perplexity, then compare answers to your Brand Fact-File. When errors appear, we remediate them and synchronize corrected facts across AI-read surfaces.

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