Measure the answer space before changing it
Build a fixed prompt universe around buyer problems, categories, comparisons and use cases. Record brand mentions, cited URLs, source domains and competitors so later movement has a reference point.
GEO · AI search · Technical SEO · Measurement
Baseline
Sources
Implementation
Evidence
Measurement
Generative engine optimization services built around measurable AI visibility.
I help B2B, SaaS and WordPress teams understand where they appear in ChatGPT, Perplexity, Gemini and other AI-assisted research journeys, why particular sources are being cited, and what can be improved across content, technical search foundations and third-party evidence. The work starts with a fixed baseline — not promises about “ranking #1 in AI.”
Map what AI systems actually cite
Identify which owned pages and third-party domains repeatedly appear for the prompt set. That separates a content gap from an authority, distribution or technical retrieval problem.
Make useful pages easier to retrieve and quote
Answer-first structure, evidence, internal links, schema, metadata, crawlability and content consolidation are implemented on the pages most likely to influence the buyer journey.
Strengthen signals outside your own site
Find legitimate publications, directories, communities and comparison surfaces already used as sources, then close factual gaps without spam or manufactured citations.
Repeat the same test, then tie it to business data
Rerun the fixed prompt set and track mentions, citations and referral sessions separately from demos, leads or revenue. GEO only matters if visibility can be connected to a useful outcome.
What the service does
Treat AI visibility as a measurable search problem, not a new kind of magic.
Generative engine optimization sits on top of search fundamentals, but the unit of analysis changes. Instead of looking only at a ranked list of URLs, we also look at the answer: which brands are mentioned, which pages are cited, which source domains are trusted and which facts are consistently retrieved.
SEO foundation
Make the site eligible to be found and understood
AI visibility does not excuse crawl, indexation or rendering problems. Canonicals, internal linking, clean templates, useful titles, structured data and strong page-level relevance still matter because answer engines often rely on search and web retrieval layers.
- Crawl and indexation review
- Canonical and duplicate-content checks
- Internal-link and information architecture
- Entity and schema consistency
- Performance on important templates
For the underlying implementation work, see WordPress technical SEO.
GEO layer
Understand the sources behind the generated answer
Two sites can rank similarly in Google and still be represented differently in AI answers. GEO adds prompt-level monitoring, source analysis and citation readiness so we can see whether the missing signal is on your site, in third-party sources or in how the category itself is described online.
- Commercial prompt universe
- Brand and competitor mention baseline
- Cited URL and source-domain mapping
- Content and evidence gaps
- Repeatable before/after measurement
Deliverables
A GEO engagement should leave you with artifacts your team can inspect.
“We optimized for AI” is not a deliverable. A useful program produces a baseline, a prioritized implementation plan, changed pages, source opportunities and a measurement loop.
Buyer questions grouped by intent
A controlled set of prompts covering problem discovery, category research, alternatives, comparisons, implementation questions and vendor selection. Prompts are kept stable enough to measure directional change over time.
Mentions, citations and source domains
For each prompt, record whether the brand appears, which owned URL is cited when it does, which competitors appear and which third-party domains repeatedly shape the answer.
Where the answer is getting its facts
Cluster cited sources into owned content, publishers, review sites, directories, documentation, communities and other surfaces. This prevents wasting time publishing another page when the actual gap is external evidence.
Remove retrieval and understanding friction
Review crawl/index behavior, canonicals, rendered HTML, schema, author/entity clarity, internal links, metadata and template consistency. Technical fixes are implemented where they materially affect discoverability or interpretation.
Upgrade pages that can become source material
Prioritize pages where the site can contribute a clearer definition, comparison, implementation answer, first-party evidence, case study or original data point instead of publishing generic volume.
Rerun the same universe and compare
Track changes in mention rate, citation rate, cited owned URLs and source mix. AI referral sessions and conversions are reviewed separately where analytics can identify them.
Measurement
If the prompt set keeps changing, the “improvement” is impossible to trust.
AI outputs are probabilistic and can vary by model, location, account context and time. The answer is not to pretend that variability does not exist. The answer is to control what we can control in the measurement design.
Primary GEO metrics
- Brand mention rate: how often the brand is named across the fixed prompt universe.
- Owned citation rate: how often an answer cites a page on your domain.
- Cited-page distribution: which owned URLs actually earn references.
- Competitive share of voice: frequency of your brand versus the comparison set.
- Source-domain mix: which external domains repeatedly influence the category.
Business metrics stay separate
An AI citation is not automatically a lead. I keep visibility metrics separate from acquisition and conversion metrics so the reporting does not manufacture causality.
- Referral sessions from identifiable AI platforms
- Landing pages reached from those referrals
- Demo/contact events attributed in analytics
- Qualified lead or pipeline data when the CRM can connect it
The reporting should show correlation and observed paths, not claim that a model mention caused a sale when the data cannot prove it.
Citation-ready content
Give retrieval systems a better reason to use your page.
The goal is not to write robotic “AI SEO” copy. It is to make the useful part of the page easy to find, verify and attribute.
Patterns I look for
- Answer the core question early, then support it with detail.
- Use precise definitions, units, dates and scope where they matter.
- Show first-party evidence: screenshots, methodology, examples, datasets or case studies when available.
- Keep author, company, product and service entities consistent across pages.
- Link to primary sources for claims that are not yours.
- Consolidate thin overlapping pages instead of creating ten near-duplicates.
- Use FAQ and structured data only when it accurately represents visible content.
The supporting article GEO strategy guide goes deeper into what to evaluate when comparing approaches and providers.
Technical GEO
There is no special AI tag that replaces technical SEO.
The strongest technical work is boring in the best way: make the important content crawlable, renderable, internally connected and unambiguous, then expose machine-readable context where it is honest and useful.
Crawl & indexation
Check robots rules, canonical targets, redirects, duplicate parameters, JavaScript rendering and XML sitemaps. A page that search systems cannot reliably discover is a poor GEO candidate.
Structured data
Use schema to clarify real entities and visible content — services, people, organizations, breadcrumbs, articles and FAQs — without inventing ratings, reviews or facts that are not on the page.
Internal architecture
Connect service pages, supporting guides, cases and definitions so important entities and topics are not isolated. Internal anchors should describe the destination instead of repeating the same commercial keyword everywhere.
Performance
Keep key templates usable and fast. See the Core Web Vitals optimization service when field performance is part of the search problem.
llms.txt
I treat llms.txt as optional machine-facing documentation, not as a ranking switch. It can summarize identity and key resources, but it does not replace crawlability, useful pages or real authority.
Measurement automation
Where manual checks become repetitive, the monitoring layer can be automated through APIs and workflow tools. Related: WordPress AI automation.
Off-site evidence
Sometimes the page you need to improve is not on your domain.
If answer engines repeatedly cite comparison sites, respected publications, documentation or communities for a category, publishing another self-authored claim may not close the gap.
Source participation, not source spam
The practical work is to identify where accurate information about the category belongs and improve your presence there when you have a legitimate reason to participate: product directories, partner pages, expert contributions, original research, documentation, public case studies or community answers.
I do not recommend manufactured reviews, fake community participation, citation farms or mass AI-generated “parasite” pages. Those create reputation and platform risk while making measurement noisier.
30-day pilot
Start with a bounded experiment before turning GEO into a retainer.
For most teams I would start with a 30-day baseline and implementation sprint. That is enough to establish measurement, fix obvious technical/content gaps and learn which sources dominate the category without committing to an open-ended program.
Baseline and source map
Agree the buyer prompt universe, comparison set and measurement rules. Record current mentions, citations, source domains and the owned pages already appearing.
Technical and content priorities
Map gaps to specific pages and templates. Separate what needs code, what needs content, what needs consolidation and what depends on third-party evidence.
Implement the highest-leverage changes
Ship a small number of changes properly: strengthen service pages, add missing evidence, improve internal links/schema and create or upgrade the supporting resource that best matches the source gap.
QA, rerun and handover
Validate crawl/index behavior, rerun the same prompt set, document what moved and what did not, and decide whether the next investment belongs in owned content, technical work or external source participation.
Fit
Best for teams with a real category, real expertise and something worth citing.
Good fit
- B2B or SaaS products with an identifiable research journey.
- Brands already investing in SEO/content but unclear about AI visibility.
- Teams with useful product knowledge, case studies, documentation or experts that can become evidence.
- WordPress sites where technical and content changes can actually be implemented.
- Marketing teams that want a baseline and repeatable measurement rather than a vanity dashboard.
Bad fit
- You want a guaranteed ChatGPT citation or “#1 AI rank.”
- The product has no clear positioning, proof or useful information to expose.
- The strategy is mass publishing generic AI copy.
- No one can implement technical/content changes after the audit.
- You need attribution precision that current AI referral and answer data cannot support.
FAQ
Generative engine optimization services: common questions.
Generative engine optimization, or GEO, is the work of improving how clearly a brand, product or expert is represented in AI-generated answers. It combines search foundations, entity clarity, citation-ready content, source analysis and repeated measurement against a fixed prompt set.
SEO primarily optimizes discoverability and ranking in search results. GEO also studies which sources AI systems use, whether a brand is mentioned or cited in answers, and how owned content can become easier to retrieve, understand and reference. The two overlap heavily and should share technical and content foundations.
No. AI systems choose and change sources independently, and outputs can vary by model, prompt, location and time. A serious GEO engagement can improve the inputs you control and measure visibility consistently, but it cannot guarantee a citation or fixed position.
I define a fixed set of commercially relevant prompts, record brand mentions, cited URLs and source domains, then repeat the same measurement after implementation. Referral traffic and downstream conversions from AI platforms are tracked separately where analytics data is available.
No. An llms.txt file can make site intent easier for some tools to inspect, but it is not a guarantee of inclusion or ranking. Crawlability, indexation, useful content, entity clarity and trustworthy third-party sources remain more fundamental.
Yes. WordPress gives direct control over templates, internal links, structured data, metadata, crawl behavior and content architecture. Where the work needs custom measurement, feeds or automation, those can be implemented with plugins or API workflows.
Want a baseline before spending on GEO?
Send me your site, the product/category you want to be associated with and the competitors buyers usually compare. I’ll scope a fixed prompt baseline and the smallest sensible first implementation.
Related services
GEO works best when search, content and implementation live in the same system.
These pages cover the technical and AI layers that usually sit underneath an AI-visibility program.
Search foundationWordPress technical SEOCrawl, indexation, schema, architecture and implementation.AutomationWordPress AI automationLLM workflows, RAG, APIs and measurable automation.AI productWordPress AI chatbot developmentOpenAI, RAG, actions, guardrails and production integration.ImplementationCustom WordPress pluginsBuild measurement, feeds and custom workflows when off-the-shelf tools do not fit.PerformanceCore Web Vitals optimizationField-data performance for search-critical templates.White-labelFor agenciesSenior WordPress and AI capacity delivered under your brand.
