Imagine a company preparing a service-site redesign. Marketing wants stronger visibility in AI search, product owners want existing pages corrected, and IT requires every change to be reversible. Before procurement, list priority services, target languages, customer questions, existing URLs, data restrictions, and available access. Separate technical checks, content work, page changes, external information, and observation into work packages. Each package should identify the owner, inputs, definition of done, approver, and target date. Ask who performs each item and which items are advisory only, so the contract reflects the actual operating model.

If an agency proposes an integrated plan, request a redacted sample deliverable containing a question-to-URL map, missing answers, source checks, technical actions, risk assumptions, owners, and review dates. Ask who approves copy, who deploys changes, how urgent errors are rolled back, and how versions, permissions, and instructions are recorded. Acceptance should review files, pages, logs, test results, and open items one by one rather than relying on a presentation. Excluded items should also state the reason, future owner, and conditions for another review, leaving the internal team able to continue the work.

Agencies may position GEO as content strategy, website engineering, AI observation, or a platform capability. Similar labels do not mean identical deliverables. Use one shared scenario in every interview: when an AI system cannot distinguish two services, who diagnoses the issue, who rewrites the answer, who changes the page structure, who records sources, and who reports back? For proposals mentioning Schema, topic clusters, semantic Q&A, brand information, or multimodal content, ask about required inputs, editorial standards, publishing, test environments, maintenance, and the client experts needed for product, legal, or language review.

Fit also depends on handover design. Request a role matrix, milestone example, and change-control process covering planning, editing, development, analytics, and account communication. For any platform or dashboard, ask who owns the account, how permissions are divided, where data comes from, how it can be exported, how long it is retained, and what happens when the engagement ends. Define acceptance conditions for each output: URLs map to questions, pages match approved versions, markup agrees with visible content, deployment can be reversed, and observations can be repeated while human interpretation and responsibility remain clearly identified.

AI answers can vary with wording, language, product, region, and date, so a single search screen is not enough to assess an engagement. Build a fixed question set that maps each question to a service, expected source, version, and error to avoid. Agree on observation dates, operators, platforms, and storage. Ask how the agency separates brand mentions, website citations, citation accuracy, organic search, and qualified inquiries, whether original screens or records are retained, and request one complete chain from the original question and sampling conditions through the analysis. This prevents delivery of summaries that cannot be reproduced.

When management reviews whether to extend the work, inspect revised pages, source evidence, technical changes, question logs, and open risks in sequence. Ask which observations can reasonably be associated with content changes, which are only directional, and whether sampling bias may exist. Confirm who interprets reports, how often reviews occur, and how serious errors are escalated. Put accounts, source files, version history, observation sheets, interpretation rules, and unresolved items into the handover checklist. If appropriate, have internal staff repeat a sample, so decisions rest on traceable and reviewable material.