{"id":2647,"date":"2026-10-04T15:10:33","date_gmt":"2026-10-04T15:10:33","guid":{"rendered":"https:\/\/digimanmarketing.com\/blog\/?p=2647"},"modified":"2026-10-04T15:10:33","modified_gmt":"2026-10-04T15:10:33","slug":"aeo-geo-services-deliverables-checklist","status":"publish","type":"post","link":"https:\/\/digimanmarketing.com\/blog\/aeo-geo-services-deliverables-checklist\/","title":{"rendered":"AEO and GEO Services: 12 Deliverables to Check Before Hiring an Agency"},"content":{"rendered":"

An AEO and GEO proposal should specify the work an agency will deliver, the evidence you will receive and how progress will be measured.<\/strong> Check technical access, useful content, accurate business information, linking, appropriate markup and a repeatable visibility sample. None of these deliverables guarantees inclusion in an AI answer.<\/p>\n

This buyer checklist helps you evaluate an AI visibility proposal before hiring. It assesses delivered work rather than ranking agencies. AEO is commonly used for answer engine optimization and GEO for generative engine optimization; providers use these labels differently, so request a concrete scope instead of assuming a standard package.<\/p>\n

Define the scope and its relationship to SEO<\/h2>\n

Ask which platforms, markets, languages and business questions the proposal covers. Google AI Overviews, Google AI Mode and other assistants are different environments. A finding about one does not establish performance in another.<\/p>\n

Google\u2019s AI features guidance<\/a> says existing SEO fundamentals remain relevant and special AI markup or new AI text files are not required for its Search AI features. This is Google-specific guidance; other providers\u2019 access rules should be checked separately.<\/p>\n

Identify overlapping SEO work so it is not billed twice without explanation. Our SEO package comparison guide<\/a> can help separate audits, implementation, content and reporting responsibilities.<\/p>\n

12 AEO and GEO services deliverables to check<\/h2>\n

1. A scoped AI visibility baseline<\/h3>\n

Actual output:<\/strong> A baseline report naming platforms, audience, locale, business questions and observation dates.<\/p>\n

Evidence:<\/strong> Exact prompts, collection conditions, response records and a definition of a mention versus a linked citation.<\/p>\n

Acceptance criterion:<\/strong> The sample and its limits are explicit; no claim of measuring every AI answer.<\/p>\n

2. Crawlability and indexing review<\/h3>\n

Actual output:<\/strong> A prioritized list of affected URLs, access barriers and indexing concerns.<\/p>\n

Evidence:<\/strong> HTTP checks, rendered content where relevant, robots and indexing directives, plus authorized search-tool evidence.<\/p>\n

Acceptance criterion:<\/strong> Observations distinguish technical accessibility from confirmed indexing and serving.<\/p>\n

3. Business and entity information consistency<\/h3>\n

Actual output:<\/strong> An inventory of brand, service, location and contact information across the agreed sources.<\/p>\n

Evidence:<\/strong> Page-level discrepancies and verified business facts supplied by the client.<\/p>\n

Acceptance criterion:<\/strong> Corrections reflect the real business; locations, credentials and associations are not invented.<\/p>\n

4. Reader-task and page mapping<\/h3>\n

Actual output:<\/strong> A map connecting customer questions to useful existing pages or justified content gaps.<\/p>\n

Evidence:<\/strong> Query purpose, page evidence and explanation of why a new page is needed.<\/p>\n

Acceptance criterion:<\/strong> Similar questions do not automatically generate competing pages or city duplicates.<\/p>\n

5. Content improvement briefs<\/h3>\n

Actual output:<\/strong> Page-specific recommendations that make answers accurate, clear and useful.<\/p>\n

Evidence:<\/strong> Before\/after examples, reviewed business claims and the intended reader task.<\/p>\n

Acceptance criterion:<\/strong> The brief specifies actual changes and approvals rather than a universal word-count formula.<\/p>\n

6. Evidence and original-information plan<\/h3>\n

Actual output:<\/strong> A plan to add genuinely available product details, methods, examples or business evidence.<\/p>\n

Evidence:<\/strong> Named information owners, source links, collection method and review responsibilities.<\/p>\n

Acceptance criterion:<\/strong> First-hand results are used only when documented; estimates and examples are labelled.<\/p>\n

7. Internal linking and navigation changes<\/h3>\n

Actual output:<\/strong> A proposed or implemented link map connecting related customer tasks.<\/p>\n

Evidence:<\/strong> Source and destination URLs, link purpose and post-change checks.<\/p>\n

Acceptance criterion:<\/strong> Links work and help users find useful pages; fixed exact-match anchor quotas are avoided.<\/p>\n

8. Appropriate structured-data review<\/h3>\n

Actual output:<\/strong> An inventory of relevant markup, errors and proposed corrections.<\/p>\n

Evidence:<\/strong> Parsed markup, visible-content comparison and current feature documentation.<\/p>\n

Acceptance criterion:<\/strong> Markup accurately describes the page; vocabulary validity and Google feature eligibility are checked separately.<\/p>\n

9. Platform-specific access and control review<\/h3>\n

Actual output:<\/strong> A documented review of relevant crawler or content-control settings for the named platforms.<\/p>\n

Evidence:<\/strong> Current official provider guidance and the intended access, search or training purpose.<\/p>\n

Acceptance criterion:<\/strong> Changes are approved and the report does not treat all bots or AI uses as equivalent.<\/p>\n

10. Repeatable prompt-sampling method<\/h3>\n

Actual output:<\/strong> A fixed measurement protocol with agreed prompts and recheck conditions.<\/p>\n

Evidence:<\/strong> Prompt list, platform or mode, dates, language, location when known, session conditions and repeat observations.<\/p>\n

Acceptance criterion:<\/strong> Changes are reported within the sample; instability and unknown conditions are disclosed.<\/p>\n

11. Referral and business-outcome measurement<\/h3>\n

Actual output:<\/strong> A measurement plan for identifiable AI referrals and meaningful actions.<\/p>\n

Evidence:<\/strong> Analytics configuration, available source records, enquiry or purchase definitions and tracking tests.<\/p>\n

Acceptance criterion:<\/strong> Referrals, mentions, citations and conversions remain distinct; missing attribution is acknowledged.<\/p>\n

12. Implementation, reporting and handover<\/h3>\n

Actual output:<\/strong> A delivery plan with owners, approvals, completed changes and the next review.<\/p>\n

Evidence:<\/strong> Change log, tested URLs, unresolved dependencies and access or asset inventory.<\/p>\n

Acceptance criterion:<\/strong> The client can distinguish recommendations from implemented work and retain agreed documentation.<\/p>\n

These are editorial procurement criteria, not twelve ranking factors prescribed by a search engine. The appropriate depth depends on your site and goals. An agency should explain why a deliverable matters and which work is outside the agreed scope.<\/p>\n

What useful sample evidence looks like<\/h2>\n

Illustrative finding:<\/strong> \u201cThe agreed service page does not state the eligible customer locations. The business confirms its actual coverage, and the editor proposes a reviewed clarification. The published change will be checked for accuracy and usability.\u201d This is a fictional example of a concrete content deliverable, not proof that location wording causes AI citations.<\/p>\n

Illustrative visibility record:<\/strong> \u201cWithin 20 agreed prompts on one named platform and mode, collected on the recorded dates, the brand was mentioned in 4 responses and its website linked in 2. Some responses differed on repeat checks.\u201d These invented figures demonstrate reporting format only; they are not Digiman results or industry benchmarks.<\/p>\n

Ask for a redacted report, a sample change log and a page-specific brief. A screenshot showing a brand once is an observation. It does not establish stable visibility, coverage across all prompts or commercial impact.<\/p>\n

Structured data: demand accurate implementation<\/h2>\n

Google\u2019s structured-data guidelines<\/a> require relevant, accurate markup consistent with page content. A valid JSON file alone does not prove suitability or guarantee a rich result.<\/p>\n

Request the types proposed, the pages affected and the actual visible information being described. Reject invented ratings, unsupported awards or claims that one schema type unlocks every assistant. Explain how the agency will prevent conflicting markup from themes, plugins or custom code.<\/p>\n

Content should also be useful without relying on hidden markup. Google\u2019s people-first content guidance<\/a> supports reliable information and value for readers. Evidence, clear conditions and original business information are useful editorial improvements; they are not guaranteed citation triggers.<\/p>\n

Reporting limits: mentions are not sales<\/h2>\n
\n\n\n\n\n\n\n\n\n\n
Metric<\/th>\nWhat it records<\/th>\nWhat it does not establish<\/th>\n<\/tr>\n<\/thead>\n
Brand mention<\/td>\nName appears in an observed response<\/td>\nA website link, endorsement or referral<\/td>\n<\/tr>\n
Linked citation<\/td>\nA source link appears in the sampled response<\/td>\nClicks or sustained inclusion<\/td>\n<\/tr>\n
Identifiable referral<\/td>\nA recorded visit with available source information<\/td>\nAll AI-driven visits or causal incremental traffic<\/td>\n<\/tr>\n
Business outcome<\/td>\nAn agreed enquiry or purchase recorded by the business<\/td>\nComplete attribution to one AI interaction<\/td>\n<\/tr>\n
Sample visibility rate<\/td>\nObserved count divided by the defined sample denominator<\/td>\nShare of every relevant AI answer in the market<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Record repeat checks and changes in collection conditions. Answers may vary across time, sessions, wording and platform modes. If a tool supplies a proprietary score, ask for the sampling method, denominator and limitations before treating it as a business KPI.<\/p>\n

For Google Search AI features, the reviewed official documentation describes their traffic within Search Console\u2019s overall Web reporting. Do not present that total as a separately verified AI-only channel. Use the available measurements honestly and explain gaps.<\/p>\n

Compare two AI visibility proposals on the same basis<\/h2>\n
\n\n\n\n\n\n\n\n\n\n\n
Comparison field<\/th>\nQuestion for each agency<\/th>\nEvidence to retain<\/th>\n<\/tr>\n<\/thead>\n
Coverage<\/td>\nWhich platforms, audience questions and languages are included?<\/td>\nWritten scope and prompt sample<\/td>\n<\/tr>\n
Technical work<\/td>\nWill you implement fixes or only recommend them?<\/td>\nAffected URLs, owner and verification plan<\/td>\n<\/tr>\n
Content<\/td>\nWho provides facts, writes, reviews and publishes?<\/td>\nWorkflow, briefs and approval responsibilities<\/td>\n<\/tr>\n
Measurement<\/td>\nWhat can be observed reliably and what remains uncertain?<\/td>\nDefinitions, collection method and limitations<\/td>\n<\/tr>\n
Costs<\/td>\nWhich setup, recurring, tool and development charges apply?<\/td>\nItemized current quote<\/td>\n<\/tr>\n
Handover<\/td>\nWhat access and documentation do we retain?<\/td>\nAccount inventory and exit process<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Illustrative comparison:<\/strong> one provider includes an audit and monthly prompt checks but leaves publishing to your team. Another includes approved page updates but uses a narrower visibility sample. Neither is automatically better. Compare the needed work, client capacity and evidence you will receive.<\/p>\n

Promises that need scrutiny<\/h2>\n