THE QUESTIONS
YOU'RE ABOUT
TO ASK US.
What is an AI visibility audit? What does GEO actually mean? When does a workflow need an agent and when is a plain old automation enough? Are we ready to ship AI yet, or do we need to fix the data layer first?
Every operator we've worked with arrived with a version of these. So we wrote the answers down — the same answers we'd give you on a discovery call, just without the call. Read what fits. Ignore the rest.
AI agents vs automations vs internal tools — which one do you actually need?
Automations execute fixed workflows. Agents reason over tool calls. Internal tools wrap one or both with a UI. The right answer depends on the failure mode you can tolerate, the conditional branching in the workflow, and how often the workflow runs. Here's the decision tree.
AI readiness checklist for operators past PMF
Eight binary questions that determine whether you should ship AI now or harden your data layer first. Designed for post-PMF operators who have the demand signal but not yet the operational substrate.
AI visibility audit vs SEO audit — what's actually different?
Same site, different scoring functions. SEO measures Google ranking signals. An AI visibility audit measures whether ChatGPT, Claude, Perplexity, Gemini, Copilot, and Grok cite your brand. Here's the comparison the way operators need it.
What metrics matter for AI visibility?
Six metrics, ranked by leverage. Citation share-of-voice, schema coverage, AI-traffic share, response quality, crawler accessibility, third-party citation density. The ones operators measure tell you which engagements actually moved the needle.
Does my company actually need an AI agent?
If a workflow has more than three conditional branches, runs more than fifty times per month, and consumes more than twenty dollars of human time per run — it needs an agent. Below that threshold, a deterministic automation is the better tool. Above it, an agent earns its keep.
GEO vs SEO: same inputs, different outputs
SEO optimizes for ranked links. GEO optimizes for cited answers. Same page can win one and lose the other — they reward different signals, surface in different products, decay on different cycles. Here's the side-by-side.
How hospitality brands increase direct bookings with AI
Where the funnel actually leaks (qualification gaps, the 60-minute response benchmark, the late-night decision window) and which AI workflows recover the leak. Drawn from two shipped audits — a Hudson Valley wedding venue and a coastal Carolina inn.
How law firms appear in ChatGPT and Perplexity answers
Drawn from two shipped audits — a Charlotte estate-planning firm and a Savannah personal-injury firm. Three things stack: credentials marked up in schema, FAQPage on every practice-area page, and either fresh content or a structured case-results hub. Most firms have one of three. The cited firms have all three.
How long does an AI visibility audit take?
Five business days at Doxia Axis, intake to dossier delivery. About 90 minutes of operator time over the full window. Most agencies take three to six weeks for the same scope. Here's why ours runs faster, and what each day actually contains.
How do I measure AI citation share-of-voice?
Five steps. One operator afternoon. Six engines, twenty to thirty queries, the results recorded verbatim. Here's the exact methodology Doxia Axis uses inside every audit, ready for any operator to run on their own brand today.
What does an AI visibility audit cost?
At Doxia Axis, the audit is free. Five business days, fourteen pages, six AI engines tested, every gap quantified in dollars. Most agencies charge $1,500 to $7,500 for the same scope. Here's why we don't, and what's in the dossier.
What is a 14-day AI sprint?
It's a two-week fixed-scope engagement that ships one production AI workflow live on your stack. Day 1 kickoff. Day 7 checkpoint. Day 14 walkthrough. Ship-date guarantee with daily credits if missed. Tier 2 unit-of-shipping at Doxia Axis. The alternative to six-month transformations that don't actually ship.
What is an AI concierge for boutique hotels and inns?
It's a property-specific assistant trained on your real room descriptions, your real policies, your real FAQ, integrated with your PMS for live availability. Handles the dozen questions guests ask 50 times a week. Escalates the genuinely-hard ones with full context. Books rooms. Different beast from a generic chatbot.
What is an AI visibility audit?
It's a structured scan of how the AI answer engines treat your business — whether their crawlers can reach you, whether your content is shaped to be quoted, and whether your brand actually surfaces when someone asks a question your business should be the answer to. Every gap quantified in revenue. Every fix sequenced by impact.
What is Doxia Axis?
Doxia Axis is an AI consultancy that runs 14-day sprints for operators past product-market fit. We focus on AI-visibility implementation and workflow automation. We audit, scope a fix, ship it, measure the revenue movement. No decks. No theatre.
What is FAQPage schema and why does it matter for AI search?
FAQPage is the Schema.org type that wraps a question-and-answer block on a page, telling AI engines exactly which Q&A pairs they can cite as standalone answers. The single most-cited schema type in ChatGPT, Perplexity, Gemini, and Google AI Overviews right now.
What is GEO (Generative Engine Optimization)?
GEO is what gets your brand cited by ChatGPT, Claude, Perplexity, Gemini, Copilot, and Grok when someone asks a question your business should be the answer to. Same foundations as SEO. Different signals. Different surface. Different game.
What is llms.txt?
llms.txt is the emerging standard for telling AI systems — ChatGPT, Claude, Perplexity, Gemini — which pages on your site matter, in what order, and how they relate. Think robots.txt for the LLM era. Lower stakes than robots.txt. Higher leverage for citation.
What schema matters for AI visibility?
The canonical schema set for AI-cited businesses — Organization, Person, Service, FAQPage, Article, BreadcrumbList, OfferCatalog, HowTo, DefinedTerm, QAPage, Review, AggregateRating. Which schemas the answer engines actually weigh, which are nice-to-have, and which are wasted markup.
What schema should a B2B SaaS deploy for AI visibility?
Eleven canonical schema types, sequenced by leverage. Organization with sameAs, SoftwareApplication with featureList, Offer per pricing tier, FAQPage on every surface page, plus the integration and review schemas the answer engines actually extract for SaaS comparison queries.
When should I do an AI visibility audit?
Five trigger conditions that mean now. Three conditions that mean wait. The 2026–2027 indexing window matters because foundation models freeze training data — whoever gets cited inside the window owns the canonical answer for years.
Who should not hire Doxia Axis?
Pre-PMF teams. Anyone who wants AI theatre for a board slide. Anyone who can't give an operator direct access to the data, people, and decisions inside the business. Bad-fit engagements hurt both sides — here's our refusal list, named honestly.