AEO and GEO Services for Verifiable AI Search Visibility

Answer Engine Optimization (AEO) is the practice of structuring and writing web pages so search features, voice assistants, and AI answer systems can extract clear, direct, and accurate responses to specific buyer questions. It emphasizes question-led headings, concise lead answers, scannable lists or comparison tables, and verified FAQ sections that match visible page content.

Generative Engine Optimization (GEO) focuses on helping large language models and retrieval-augmented AI systems understand who your business is, what services you provide, and how your pages relate to one another. It combines entity disambiguation, consistent facts across pages and public profiles, semantic internal linking, validated JSON-LD structured data, and clean machine-readable documentation.

Traditional SEO remains an essential technical foundation because crawlability, indexation, site speed, and page authority still govern whether search engines and retrieval crawlers can discover your pages. While buyers often search for AI SEO services as an umbrella term, AEO and GEO extend traditional SEO by focusing on passage-level answer clarity, cross-source entity consistency, and machine-readable context for generative synthesis.

We improve the clarity, consistency, and technical accessibility of your public website across these ecosystems. That includes clean server-rendered HTML, direct service definitions, structured FAQs, validated JSON-LD, and reviewing crawler policies by documented purpose: OpenAI’s OAI-SearchBot (ChatGPT search), GPTBot (potential model-training collection), and ChatGPT-User (user-triggered page access that is not an automatic crawler used to determine ChatGPT Search inclusion and where robots.txt rules may not apply), alongside Anthropic’s Claude-SearchBot (search indexing and retrieval), ClaudeBot (content that may contribute to model training), and Claude-User (user-directed retrieval), plus published crawler policies for other platforms. For Google AI Overviews and AI Mode, standard Search indexability, snippet eligibility, helpful content, and internal linking remain the foundation. Where your business publishes developer documentation or code repositories, we also structure markdown for coding agents such as Claude Code.

An llms.txt file is an emerging, voluntary convention and optional publishing format (llmstxt.org) placed at /llms.txt that provides a concise markdown directory of your canonical pages, documentation, and service summaries. Google Search ignores llms.txt—so publishing one neither helps nor harms Google Search visibility—and requires no special AI file for AI Overviews or AI Mode, and not every AI model or search crawler automatically reads llms.txt. It is not a ranking, citation, discovery, indexing, or eligibility signal, and is used only as an optional directory for compatible tools and developer workflows.

No. Google requires no special AI schema for AI Overviews or AI Mode, and schema markup does not trigger or guarantee AI citations. Validated Organization, Service, and WebPage JSON-LD reduce ambiguity in entity and service descriptions, while supported Search feature types such as BreadcrumbList can support standard Search appearance where applicable. Because Google discontinued FAQ rich results in May 2026, we use synchronized FAQPage JSON-LD strictly as a machine-readable representation of visible page questions rather than a Google FAQ rich-result feature.

Our baseline audit evaluates four areas: (1) how your brand and services are summarized across a defined set of commercial and comparison prompts in major AI tools, (2) whether your core entity facts are consistent across your site and public profiles, (3) where key buyer questions lack clear, extractable answers on your pages, and (4) technical barriers in rendering, robots directives, canonicals, sitemaps, and JSON-LD.

Generative AI outputs are non-deterministic and vary by model version, user session, geographic location, and time. No outside agency has proprietary access to private vendor ranking or retrieval weights. We monitor progress honestly by recording dated prompt observations as point-in-time snapshots alongside verifiable Google Search Console indexing, query impressions, and consented website enquiry trends.

Scope and timing depend on site size, template count, stakeholder review, implementation access, and remediation complexity. We provide a tailored estimate after completing the baseline review of your pages, templates, and technical architecture. Because search recrawling and model retrieval cycles operate on third-party vendor schedules, we never promise fixed delivery, ranking, indexing, or citation timelines.

No. Third-party search engines and AI platforms control their own retrieval pipelines, training data, safety filters, selection, summaries, and citation logic. We never promise guaranteed citations, rankings, recommendations, indexing, traffic lifts, lead totals, or revenue outcomes. Our work supports technical eligibility and improves the clarity and consistency of information available on your public website.

Yes. You can engage Prateeksha for a standalone AI search visibility audit and strategy blueprint, a scoped implementation project covering core service pages, FAQs, schema, and optional llms.txt documentation, or an ongoing retainer that combines AEO, GEO, and technical SEO refinement as your offerings expand.