1. What should the AI visibility SLA cover?
An AI visibility SLA should cover measurement, evidence, reporting, response times and ownership, not a promise that an engine will mention the brand. Start by naming the seven engines in scope: ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode. State whether the agreement covers all seven or a defined subset, and record any exclusions, including Microsoft Copilot if it is not measured.
Define the business outcome in operational terms. The SLA may require a daily check of selected buyer questions, a weekly report, an alert when visibility falls below an agreed threshold, or a documented recommendation within a specified working period. Separate those deliverables from outcomes outside the team's control, such as an engine changing its model or source selection.
Write down what counts as visibility. A brand mention, a linked citation, a recommendation, a position in a comparison, and an accurate description are different events. The agreement should say which events are measured and whether a correct citation matters more than an unlinked mention. This prevents a service from meeting a vague visibility target while failing the buying questions that matter.
For more context, read How Often Should I Check Ai Visibility.
2. Which buyer questions should enter the SLA?
The SLA should use a fixed prompt set based on real buyer questions, not a large collection chosen only because it is easy to track. Group questions by intent, such as category discovery, problem solving, comparison, alternatives, implementation and purchase readiness. Include questions where the company expects to be considered and questions where competitors currently appear.
Record the exact wording, location, language, date added and business owner for every prompt. Small wording changes can produce different answers, so replacing a prompt should create a new version rather than silently changing the baseline. Mark branded and nonbranded prompts separately, but do not let branded questions stand in for category visibility.
Check whether each prompt has a decision attached to it. For example, a nonbranded comparison question might trigger a review of cited pages, while an inaccurate product description might trigger a documentation review. Remove prompts that no longer represent the market, but preserve their history. The key check is whether the set reflects the questions a prospective buyer would actually ask, rather than the questions that produce the most favorable result.
For more context, read How To Check Ai Content Visibility Across Seven Engines.
3. How should the SLA define a valid AI answer check?
A valid AI answer check needs a repeatable capture method, an evidence standard and clear treatment of variability. State whether the team records the full answer, cited URLs, engine, prompt, date, location, language and relevant account or search settings. Preserve the response that supports a reported result, because a score without evidence cannot show what changed.
Specify how repeated answers are handled. An engine may produce different wording or sources for the same question, so the SLA can require repeated checks, a recorded sample, or a status such as mentioned, cited, absent, inaccurate or ambiguous. Avoid treating a single answer as definitive evidence of a lasting change. The method should also distinguish an answer that names a brand from one that cites a page about it.
Check accessibility and reproducibility before signing. Another team member should be able to understand why an answer received its status and inspect the supporting evidence. Engine interfaces, retrieval behavior and product features change, so the SLA should allow the measurement method to be reviewed when an engine changes how it displays or cites sources.
4. Which visibility thresholds deserve an alert?
An alert threshold should identify a meaningful business or measurement change, not every fluctuation in an AI answer. Define separate conditions for loss of brand mention, loss of citation, inaccurate information, competitor substitution and a change across several priority prompts. A single missing mention may require observation, while a repeated inaccurate answer may require immediate escalation.
Set thresholds against a documented baseline and use categories rather than one blended score where possible. A company could require review when a priority prompt loses a citation repeatedly, when a group of category prompts no longer names the brand, or when a cited page changes from an owned source to an unsuitable source. The exact threshold should reflect prompt importance and evidence reliability.
Check for false alarms before finalising the rule. Exclude prompts that are under review, label newly added prompts separately and record known engine changes. The SLA should state who confirms an alert, how quickly confirmation occurs and what evidence is attached. Thresholds are decision rules, not proof that a ranking or model has permanently changed.
5. Who owns each response after an SLA breach?
Every SLA breach needs one accountable owner, one supporting team and one defined next action. Assign ownership by failure type. SEO may inspect technical and search evidence, content may correct unclear or incomplete pages, product teams may verify factual claims, and marketing leadership may decide which commercial questions take priority.
Create a response path for at least four situations: an unexplained visibility drop, an inaccurate answer, a lost citation and a monitoring failure. The path should state who validates the issue, who investigates cited and competing pages, who approves a change, and who verifies the next result. Without this separation, teams can spend the response window debating whether the result is real.
Check that the owner can act within the agreed time. A response target is not useful if the named person lacks access to pages, analytics, publishing workflows or technical support. Cituna joins answer results to Google Search Console data and provides SEO, AEO and GEO fixes, which can help teams connect an observed answer issue to a search or content investigation. The SLA should still assign decisions to the customer's own accountable team.
6. What reporting cadence should the SLA require?
The reporting cadence should match the speed of the decision, with frequent checks for important prompts and a slower review for trends and prioritisation. Specify what is checked daily, what is summarised weekly, and what is reviewed monthly if those cadences fit the business. A report should show results by engine and prompt group, not only one overall visibility number.
Require each report to include new and resolved alerts, evidence for material changes, cited competitors and pages, open investigations, recommended actions and the owner of each action. Include a change log for prompts, thresholds and methodology. The reader should be able to tell whether visibility changed, measurement changed, or the underlying question set changed.
Check whether the cadence produces decisions rather than administration. If a daily report is never reviewed, it may be better to reserve daily monitoring for high-priority prompts and use a weekly action review. Cituna tracks the seven named engines every day and shows which competitors and pages they cite instead. An SLA can use that evidence to set reporting expectations, while keeping the report focused on the buyer questions that matter.
7. Which service level should you choose for your team?
Choose the service level by the cost of a missed or inaccurate AI answer, the number of prompt groups, and the response capacity of the team. A small team may need a narrow, high-value prompt set with clear weekly action rather than broad monitoring that nobody can investigate. A larger programme may need more prompts, engine-level reporting, faster escalation and several owners.
Compare offers by included engines, tracked prompt capacity, evidence retention, integrations, recommendations and API access. Cituna includes ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews and Google AI Mode on every plan, without per-engine add-on fees. Its plans are $39, $119 and $399 a month, with the $39 plan covering 10 tracked prompts. Search Console and an MCP server are included from the entry plan, while the API is on Max.
Check fit against the SLA you wrote, not the plan label. Ask whether the included prompt capacity covers the agreed baseline, whether the evidence supports breach review, and whether your team can act on the recommendations. If the service cannot support the required engine scope or response process, lowering the target does not solve the operational gap.
8. When should the AI visibility SLA be reviewed?
Review the AI visibility SLA whenever the business, prompt set, engine behavior or response process changes, and schedule a formal review even when results appear stable. A new product, market, competitor, website structure or buying journey can make the original questions incomplete. An engine may also change how it retrieves, formats or cites information.
Check four things during each review: whether the prompts still represent buyer decisions, whether the evidence method remains reproducible, whether thresholds generated useful actions, and whether owners met response expectations. Compare the SLA's operational record with actual work completed, not only with visibility results. A team may have stable mentions but still fail because inaccurate answers were not escalated.
Use the review to retire weak prompts, add newly important questions, adjust alert rules and clarify ownership. Record the reason for every change so later reports remain interpretable. Do not rewrite the baseline after an adverse result. Preserve the earlier definition, label the new version and state when it took effect. That distinction prevents a service-level review from becoming a way to make missed commitments disappear.
Related reading
- How To Improve Ai Search Visibility With Answer Pages
- Ai Visibility Services For Small Businesses What To Buy
Sources consulted
- OpenAI Platform Documentation (platform.openai.com)
- Google Search Central (developers.google.com)
- Google Search Help (support.google.com)
- Anthropic (anthropic.com)
Drafted with AI assistance from our own research and Search Console data, and reviewed by Rahul A before publishing. Rules and prices change; check the linked official source before you act.