AEO Services That Turn AI Visibility Data Into Website and Source Actions
UnderAI helps brands diagnose how they appear in AI-generated answers across systems such as ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews, identify why competitors or other sources are used instead, and improve the official pages and public evidence that support better answers.
Optimization targets: ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews.
Start with the problem, not a package
UnderAI can be purchased as a software-and-service system rather than as services alone. UnderAI GEO Workspace supplies ongoing AI Performance Tracking across projects, Prompts, platforms, competitors, Answer Snapshots, Sentiment, and Citations. The managed modules connect that evidence to implementation. Every engagement should leave the client with a reviewed prompt system, saved response evidence, clear page owners, prioritized actions, and a retest plan—not just a composite visibility score. Our services are built for B2B SaaS brands, ecommerce and DTC brands, fintech and Web3 companies, AI tools and software companies, agencies and consultants, and enterprise marketing teams.
UnderAI is a fit when your team needs to answer one or more of these questions:
- Why is our brand absent from product or provider recommendations?
- Why do AI systems describe our category or capabilities incorrectly?
- Which competitors and pages are being cited instead?
- Which official page should answer each high-value question?
- Which external sources are shaping the answer?
- Which changes can we test without confusing correlation with causation?
Choose the UnderAI software and service modules you need
Software | UnderAI GEO Workspace
Use the Workspace when the team needs ongoing product access: Brand Workspaces, market-specific Tracking Projects, Prompt Groups, enabled AI platforms, competitors, daily results, Coverage, Visibility, TOP3, Sentiment, Answer Snapshots, and Citation evidence. Access is invitation-based and public list pricing is not currently published; confirm platform, market, project, user, export, and service scope during procurement.
Prompt Research
We combine relevant search demand, observed AI prompts, customer language, and approved business controls. Near-duplicates are grouped by the object being discussed, the user’s task, and the question angle.
Deliverables may include:
- full prompt library with source labels;
- business classification and review flags;
- fixed monitoring panel with version and cadence;
- prompt-to-page owner map.
AI Visibility Audit
We collect complete responses across agreed platforms and preserve status, timestamp, brand and competitor mentions, recommendation role, description facts, and citation URLs.
The baseline distinguishes:
- successful response;
- platform or route failure;
- empty or unavailable result;
- successful answer with no target mention;
- successful answer with no citation.
Website AI SEO
We assess whether important pages are accessible, clearly owned, answer-ready, internally connected, and supported by current facts. We separate technical access, entity clarity, content completeness, and source authority instead of treating them as one “AI SEO score.”
Deliverables may include:
- website owner and content Gap matrix;
- page-level rewrite or new-page plan;
- copy, headings, summaries, tables, FAQs, and internal links;
- schema and technical recommendations;
- redirect and publication Gates.
Citation Strategy
We trace each source back to the prompt and response that used it, then classify official competitor pages, external publishers, communities, and other source types.
The goal is to identify evidence gaps and realistic source opportunities. We do not treat a frequently cited domain as automatically purchasable, and we do not promise that publishing on a domain will cause a citation.
AEO Content Strategy
Depending on scope, UnderAI can help produce or revise definition pages, decision guides, comparison pages, methodology pages, FAQs, service pages, source briefs, and measurement documentation.
Every public claim remains subject to a fact Gate. Product features, prices, customer outcomes, certifications, and regulated claims must be verified before publication.
Monitoring & Retesting
After an approved change is live and technically available, we rerun the same monitoring scope. We compare compatible successful response units, document other changes in the test window, and decide whether to continue, revise, or stop the tactic.
The UnderAI operating cycle
Demand → Baseline → Owner Gap → Build → Publish → Retest → Decide
Each stage has a clear acceptance signal:
| Stage | Acceptance signal |
|---|---|
| Demand | Source, entity, task, and uncertainty are labeled |
| Baseline | Planned, successful, failed, and excluded units reconcile |
| Owner Gap | Every priority question has one official owner or an explicit missing-owner decision |
| Build | Copy and claims pass the fact Gate |
| Publish | HTML, canonical, schema, sitemap, and redirects are verified |
| Retest | Same panel and compatible collection conditions are preserved |
| Decide | The result changes a page, source, product, or monitoring decision |
Tool, advisory, or managed service?
Choose a monitoring tool when
Your team already knows what to track, can review raw responses, and has owners who can act on the data.
Choose an advisory engagement when
You need a defensible baseline, page architecture, metric design, or an independent review before investing in software or a larger program.
Choose managed implementation when
The gap spans positioning, website content, technical access, third-party evidence, and cross-functional delivery.
UnderAI can also work with an existing platform. The important requirement is that the team can preserve prompt IDs, successful response units, citation URLs, and method changes. If you are comparing providers before choosing a service model, use the GEO agency selection guide.
What UnderAI will not promise
- guaranteed mentions, rankings, recommendations, or citations;
- “content poisoning,” fabricated reviews, or mass low-quality publishing;
- a live software capability that is not part of the agreed service;
- a causal claim based on one before-and-after observation;
- a zero result when the platform failed or the data was not available.
Google’s people-first guidance asks whether content demonstrates first-hand expertise and adds value beyond summaries of other sources. That standard is why UnderAI engagements preserve the response and source evidence behind every recommendation. See Google’s people-first content guidance.
What a client should receive
A complete engagement should make the work auditable after the presentation ends. The handoff normally includes:
- raw and normalized evidence locations;
- manifests and method notes;
- reviewed Prompt Library and fixed Panel;
- baseline and Gap reports;
- page roadmap and website copy;
- source strategy and ownership;
- acceptance checklist;
- retest output and next decision.
Why the service is built around evidence ownership
UnderAI’s July 2026 baseline exposed separate brand, official-page, external-source, and measurement gaps. This category baseline is not a client performance claim. The service model turns those different findings into different jobs instead of issuing one generic “publish more content” recommendation:
- Prompt Research: decide which real market and business questions deserve measurement;
- Website AI SEO and content: create an official owner for questions the site cannot answer completely;
- Citation Strategy: address the external publishers and competitor pages already supplying evidence;
- Monitoring & Retesting: rerun the same Panel without converting collection failure into a performance result.
That separation is reflected in the deliverables. A client should be able to trace every recommended page or source action back to a prompt, answer, competitor, Citation, or technical finding.
Example acceptance record
| Field | What the client should be able to inspect |
|---|---|
| Question | Exact Prompt ID, wording, source, and business task |
| Baseline | Successful answer, brand role, competitor role, and Citation URLs |
| Action | Named page or external source, owner, fact gate, and publication version |
| Retest | Same Prompt and conditions, new response evidence, and interpretation |
Frequently asked questions
What are AEO services?
AEO services help a brand improve how it is understood, compared, described, and cited in AI-generated answers. The work can include prompt research, monitoring, website content, technical access, source strategy, and retesting.
How are AEO services different from traditional SEO services?
Traditional SEO focuses on search visibility, clicks, and organic traffic. AEO adds answer-level measurement: mentions, recommendation role, description accuracy, and citations. The technical and content foundations overlap.
Does UnderAI sell AI visibility software?
Yes. UnderAI GEO Workspace is the AI Performance Tracking software product. It supports Brand Workspaces, market-specific projects, Prompt and competitor configuration, daily platform results, Coverage, Visibility, TOP3, Sentiment, Answer Snapshots, and Citation evidence. Access is currently invitation-based rather than open self-sign-up. The six managed modules are optional diagnosis and implementation services around the product; UnderAI can also work with a client’s existing auditable platform.
Can UnderAI guarantee a citation?
No. UnderAI can improve the clarity, completeness, accessibility, and public evidence around a brand, then measure whether answers change. No responsible provider can guarantee a specific model output.
What is the first deliverable?
Usually a scoped baseline and diagnosis: reviewed questions, collection evidence, current brand and competitor results, cited sources, official page owners, and a prioritized action plan.
How is success measured?
Success criteria are agreed before implementation and use compatible response units. Depending on the task, they may include strict mentions, recommendation position, description accuracy, official citations, external source adoption, qualified referral, or implementation acceptance.
Start with an AI Visibility Audit
The first engagement is designed to answer three questions: where the brand is losing, which evidence is missing, and what should be fixed first. Request an AI Visibility Audit to review your target market, website, buyer questions, current AI answers, competitors, and available proof.
- See the prompts you are losing
- Find the trust signals AI is missing
- Know what to fix first
Request received. Our team will contact you by email to discuss your brand, target market, and next step.