An example of the work, not a claimed result.
No live company was audited for this example. The scenario and observations below are hypothetical. No traffic, ranking, AI mention, or revenue improvements are claimed.
Define the review before collecting answers.
The illustrative business
A software provider selling project operations tools to consulting firms. Its prospective buyers need to understand the fit, onboarding requirements, integrations, and commercial next step.
The sample scope covers the homepage, product pages, implementation content, and contact journey. A real engagement would agree on the markets, languages, tools, and question set first.
Example buying questions
- What should a consulting firm look for in project operations software?
- How do these tools differ from a general project management app?
- What should our team prepare before a software migration?
- Which requirements should we include in a vendor evaluation?
What we would assess
Every finding needs a question, a source, and a reason it matters. A score without that context is not enough.
Business clarity
Can a buyer identify who the product serves and what it does?
Evidence to collect: Homepage and product-page copy, audience descriptions, and consistent company information.
Question coverage
Do useful pages answer the questions that shape a shortlist?
Evidence to collect: A question-to-URL map covering use cases, implementation, alternatives, and buying requirements.
Supporting evidence
Can a reader verify the claims being made?
Evidence to collect: Relevant documentation, named sources, product examples, authorship, and the dates of substantive updates.
Page accessibility
Is the essential content available on a crawlable, coherent page?
Evidence to collect: Rendered content, response status, internal links, indexing signals, and page metadata.
Answer observations
What do selected AI answers say, and which sources do they cite?
Evidence to collect: Recorded prompts, tool and date, answer excerpts, cited URLs, and repeat observations where appropriate.
Next-step clarity
Can a relevant visitor make an informed inquiry?
Evidence to collect: Visible contact routes, useful service information, working forms, and the submission confirmation.
How findings become priorities
These worked examples show the level of detail a recommendation should have. Actual priorities depend on the audit evidence.
Clarify first
Replace a generic headline with a specific offer.
- Example observation
- The hypothetical homepage says, “An easier way to manage your business.” It does not name the buyer or the product category.
- Recommended action
- Use a headline such as “Project operations software for consulting teams,” followed by a factual explanation of its core workflow.
- Acceptance check
- A reader can identify the audience, category, and use case without opening another page.
Build next
Give the implementation question its own answer.
- Example observation
- In this scenario, onboarding information exists only in sales conversations.
- Recommended action
- Create an implementation guide covering data preparation, migration responsibilities, training, and the factors that affect the rollout.
- Acceptance check
- The product owner verifies the details; the guide is linked from the product and contact pages.
Review regularly
Make important claims traceable.
- Example observation
- The example website uses broad terms such as “easy to integrate” without explaining the supported integrations or requirements.
- Recommended action
- Replace broad claims with verified documentation links, supported options, and relevant limitations.
- Acceptance check
- Each material claim has an identified source and an owner responsible for keeping it accurate.
What the handoff contains
- An evidence log with reviewed URLs and recorded observations.
- A question-to-page map and a prioritized action plan.
- Content briefs with purpose, required evidence, and suggested structure.
- Implementation notes with owners and acceptance checks.
Measurement with context
A real baseline records the question set, tools, dates, and method. Repeat checks are compared with that context, alongside relevant site visits and inquiries. A small sample of AI answers does not represent every buyer or prove that a content change caused an outcome.