For a MarTech company, a useful AI search visibility project starts with a specific buyer question. Can someone researching your product category find accurate information about your integrations, implementation requirements, pricing model, and limitations?
Generative Engine Optimization services, commonly called GEO services, aim to improve how a business’s public information can be discovered and represented in AI-assisted research. The service label alone tells you little about what a provider will deliver. A credible proposal should identify the information gaps, technical work, evidence requirements, and measurement limits behind its recommendations.
This guide explains how to assess that work for marketing automation, customer data platforms, attribution software, analytics, and related MarTech products.
What GEO services should mean in practice
In this guide, GEO means improving the accessibility, clarity, and reliability of information that may be used in AI-generated answers. It does not mean geographic targeting. Providers use GEO and Answer Engine Optimization, or AEO, with overlapping meanings, so agree on deliverables rather than relying on terminology.
A useful engagement connects conventional SEO, product documentation, editorial review, and observation of selected AI search experiences. It should help buyers verify your offering even when an AI answer never mentions your brand.
Google states that its AI Overviews and AI Mode do not require special optimization beyond established SEO practices. That guidance applies to Google Search; it should not be presented as documentation for every assistant. Read Google’s guidance for websites in AI features before approving a proposal that promises a separate technical shortcut.
Start with the MarTech buying decision
“Improve AI visibility” is too broad to guide a project. Define the decisions your content should support and the audience making them. A growth team evaluating an email automation tool needs different evidence from an enterprise architect reviewing a customer data platform.
Build a question set around product fit:
- Integration: Which systems connect, through which method, and with what restrictions?
- Implementation: What data, permissions, engineering effort, and customer responsibilities are involved?
- Commercial fit: What determines pricing, and which capabilities require a separate plan or agreement?
- Evaluation: What tradeoffs distinguish this product from alternatives for a defined use case?
- Governance: Where can buyers verify security, privacy, and data handling claims?
Use sales questions, support tickets, product documentation, and customer research where available. Treat suggested prompts as hypotheses until your team confirms that they reflect actual buying concerns.
What to include in a GEO service scope
1. A product evidence audit
Ask the provider to map each priority question to an existing source. Record whether the answer is accurate, current, publicly accessible, and approved by the person responsible for the product claim.
For integrations, distinguish a native connector from an API, a partner connector, or a custom implementation. For security and compliance, link to approved documentation and describe its scope. Avoid turning a technical capability into an unsupported certification claim.
The deliverable should be an evidence backlog: the buyer question, current page, missing detail, responsible reviewer, and proposed change. This makes the work reviewable before new pages are commissioned.
2. Technical access and indexability checks
Require platform-specific checks rather than a generic “AI crawler readiness” score. For Google, supporting-link eligibility depends on indexing and snippet eligibility. Meeting those conditions does not guarantee inclusion.
A site owner’s technical review should verify:
- The preferred URL loads successfully and is accessible to Googlebot.
- Robots rules, authentication, server errors, and unintended noindex directives do not block the intended page.
- Canonical tags identify the appropriate URL.
- Relevant crawlable pages link to it and sitemap entries use the preferred address.
- Important rendered content is accessible to Google.
- Search Console URL Inspection and indexing reports have been reviewed.
Crawler access for other platforms should be checked against their current official documentation. Do not assume that access for model training and access for search serve the same purpose. A sitemap or indexing request can support discovery without guaranteeing crawling, indexing, or ranking.
3. Content changes that resolve a real question
Prioritize pages that help a buyer make a decision: integration documentation, implementation guides, product comparisons, migration information, and clearly scoped use cases. Improve the appropriate existing page before creating another overview.
Organize information with descriptive headings and direct explanations. Tables can clarify compatibility or plan differences when the facts are verified. Structured data should reflect visible content. Google does not require special AI schema or an AI text file for its AI search features.
These are usability and accuracy choices. They should not be sold as a formatting recipe that forces citations.
4. Consistent product and company information
Check whether your website, approved directory profiles, partner pages, and public documentation agree on the company name, product category, supported capabilities, and destination URLs. Correct factual conflicts and outdated claims.
When pursuing third-party coverage, offer verifiable research, expert explanations, or useful product information. Avoid fabricated reviews and paid links intended to manipulate rankings. An agency should explain both its outreach method and the evidence it needs from your team.
A hypothetical example: answering an integration question
Consider a fictional attribution vendor whose website says only, “Connect your CRM.” A prospective customer asks whether the software supports a specific Salesforce workflow. The existing page provides too little information for that customer to judge fit.
A useful project would ask the product team to verify the connection method, supported objects, sync direction, update behavior, authentication requirements, plan availability, and known limitations. The editor would then update the integration page and link it from relevant product and implementation documentation.
If custom engineering is required, the page should say so. If a capability is still planned, it should not appear as generally available. The immediate deliverable is an accurate answer buyers can check. Any subsequent AI mention or citation is an observation to record, not a promised consequence of the edit.
Evaluate providers by evidence and acceptance criteria
Compare proposals using the same questions. Ask to see a sample audit, a proposed change, and the method used to assess its effect. A polished visibility dashboard does not establish that the underlying recommendations are appropriate.
| Workstream | Evidence to request | Acceptance criterion |
|---|---|---|
| Research | Buyer questions linked to product and audience needs | Your team can explain why each question matters |
| Technical review | Affected URLs, observed issues, and platform documentation | Findings are reproducible and assigned to an owner |
| Content | Proposed edits with sources for product claims | Product and editorial reviewers approve the facts |
| Measurement | Prompt set, test conditions, denominators, and saved observations | Another reviewer can understand how results were collected |
| Commercial scope | Deliverables, implementation responsibilities, fees, and exclusions | Both teams know what is included and who makes changes |
Ask who owns engineering fixes, how revisions are approved, what happens when a platform changes, and whether exports remain available after the engagement ends. Require substantiation for case studies, including dates, starting conditions, changes made, and measurement limits.
Reject promises of guaranteed recommendations, citations, rankings, or revenue. Outcomes depend on systems and buyer behavior the provider does not control.
Measure observations without overstating results
A useful reporting plan distinguishes a brand mention, a citation to your domain, factual accuracy, identifiable referral visits, and qualified actions on your website. These answer different questions and should remain separate.
For a controlled observation set, record the prompt, platform or product mode, date, language, location where relevant, account context, and whether web search was used or visibly occurred. Save the response and cited URLs when permitted. Keep the conditions as consistent as practical and report their limitations.
For example, a report might show how many eligible tested responses mentioned your brand and how many cited your domain. State the denominator and distinguish repeated runs from unique prompts. The resulting rate describes that sample; it is not a measurement of all buyers or the entire platform.
Check whether the answer represents capabilities correctly. A frequent mention paired with an invented integration is a problem to investigate, not a successful business outcome. Compare observations with your publishing log, but do not infer causation from a before-and-after change alone.
Google currently includes traffic from its AI features within the Web search performance data in Search Console. Do not treat that aggregate as a clean report of AI-only traffic. Similarly, unidentifiable referrals and journeys without a click limit what website analytics can establish.
Use a bounded pilot before a broad rollout
Choose one product category, one buyer group, and a manageable set of questions. Audit the existing evidence, agree on the most useful changes, assign reviewers, and capture a baseline under documented conditions.
After implementation, check that the changes are live and accurate before interpreting visibility observations. Review the workload as well as the reports: did product reviewers respond, did engineering fixes ship, and did the content resolve a documented buyer problem?
Expand only when the process produces useful, maintainable assets and interpretable evidence. A contract can commit to work and review dates; it should not promise a date when an independent platform will include your brand.
Google’s guidance on generative AI content warns against producing many pages without added user value. For MarTech teams, the practical implication is to improve product evidence and consolidate overlap before commissioning a large content batch.
Where SEO Jetty fits in the evaluation
SEO Jetty’s official website lists GEO and AEO, and its AI-Powered Search Engine Optimization service page includes GEO among its advertised capabilities. Those categories are relevant to this evaluation, but the proposal still needs to specify the work for your product, market, and website.
Ask SEO Jetty for a scoped assessment covering your priority buyer questions, existing evidence, implementation responsibilities, and reporting method. Use the same acceptance criteria you would apply to any provider.
