AI visibility is not
- A promise that a model will recommend you.
- A mass-content publishing tactic.
- A special schema trick.
- A replacement for SEO.
Service vertical — AI Search Visibility · AIO/GEO
AI Search Visibility for B2B Companies.
An AI-era discovery and demand system for B2B companies.
Improve how AI search systems discover, understand, verify, and surface your company. We build the search, entity, evidence, and conversion infrastructure that makes your expertise easier to retrieve across Google Search, AI Overviews, ChatGPT Search, Perplexity, Bing/Copilot, and other AI-assisted discovery surfaces.
Built on search infrastructure, not GEO hacks. We improve the systems AI retrieval depends on: technical search eligibility, crawlability, entity clarity, expert content, structured evidence, internal linking, and conversion automation.
Prompt-market mappingTechnical search eligibilityEntity and evidence architectureAI-surface monitoringCRM-connected conversion
ai.visibility · liverunning
The discovery system
An AI-era discovery and demand system for B2B companies.
We build the search, entity, evidence, and conversion infrastructure that makes your expertise easier for AI search systems to discover, understand, verify, and surface — then connect it to the CRM so visibility becomes pipeline.
discovery.system · examplesearch infrastructure
example operational viewillustrative — real values appear after the audit
- Priority prompt set
- 42
- Technical blockers
- 6
- Evidence gaps
- 11
- Tracked surfaces
- Selected
- Qualified visits
- Connected to CRM
Why this matters now
AI visibility is built on SEO fundamentals, then extended to AI search surfaces.
Google AI features rely on core Search systems: indexed pages, crawlable architecture, snippet eligibility, and high-quality content. Other AI systems apply comparable trust and retrieval logic. We improve the shared foundation first, then add AI-specific observation and conversion operations so visibility is measurable and repeatable.
What this is — and is not
AI visibility is a measurable effort — not a promise that a model will recommend you.
AI visibility is
A measurable effort to improve the discoverability, clarity, evidence quality, search eligibility, and conversion readiness of the pages that represent your expertise.
Google states that AI Overviews and AI Mode have no special requirements or separate “magic optimization” — what matters is fundamental SEO practices and genuinely helpful content. Structured data is a supporting layer that must match visible content, not an independent AI-ranking switch.
How we do it
The AI visibility operating pipeline.
AI visibility is built on search infrastructure, not GEO tricks. We run prompt-market modelling, technical eligibility, entity and evidence architecture, structured-data validation, sampled AI-surface observation, and CRM-connected conversion as one operating pipeline — with a named owner and a shipped artifact at each step.
STEP 01
Outcome
Prompt market model
The buyer prompts that decide your category, modelled
We work backwards from the buyer. We define category, commercial, comparison, problem-aware, and multilingual prompts your prospects use across AI search — so every later step optimizes against a real prompt set, not guessed keywords.
- Prompt taxonomy
- Intent + stage labels
- Multilingual variants
STEP 02
Outcome
Baseline observation
Where you stand today, recorded
We record current page visibility, citations, mentions, competitor sources, query intent, language, and region across a sampled prompt set — a starting point measured, not assumed.
- Baseline (per surface, per prompt)
- Competitor source map
- Quick-win shortlist
STEP 03
Outcome
Technical eligibility
Important pages are crawlable, indexable, and snippet-eligible
We audit crawlability, indexing, rendering, canonicals, sitemaps, internal links, robots directives, and page speed on the pages that matter commercially — the foundation AI features rely on.
- robots + sitemap checks
- Canonical + indexing checks
- Crawler fetch verification
STEP 04
Outcome
Entity architecture
An entity graph search and AI systems can traverse
We clarify Organization, services, people, proof, markets, locations, case studies, and the relationships between them across the site, with sameAs alignment where it is verifiable.
- Organization + Person graph
- sameAs alignment
- Service definitions
STEP 05
Outcome
Evidence architecture
Verifiable, citable pages for the highest-value queries
We build service pillars, technical explainers, comparisons, case studies, author context, and visible proof — structured so claims sit in citable, directly-answering passages.
- Priority page templates
- Direct-answer structure
- Internal link mesh
STEP 06
Outcome
Structured data validation
Validated markup that matches visible content
We deploy only markup that matches visible content — Organization, Service, Article, BreadcrumbList, FAQPage where appropriate — validated against Schema.org and Google's Rich Results test. Structured data is a supporting layer, not an AI-ranking switch.
- Validated JSON-LD
- Rich Results checks
- Content-match review
STEP 07
Outcome
AI-surface observation
A sampled prompt set tracked across selected surfaces
We track a stable, sampled prompt set across selected engines, locations, and languages, and review citations, mentions, and factual errors. Where useful we verify technical accessibility of individual crawlers — which does not guarantee appearance in answers.
- Sampled monitoring set
- Per-prompt log
- Factual-error flags
STEP 08
Outcome
Conversion operations
AI-search traffic captured into a process, not a contact form
We route qualified AI-search traffic through answer-first landing pages, lead enrichment, CRM workflows, sales alerts, and reporting — the same operational infrastructure we build for our automation clients.
- Answer-first landing pages
- CRM intake + routing
- Follow-up + reporting
Google does not require special GEO tricks for AI Overviews or AI Mode: the base is indexability, crawlability, helpful content, internal linking, and correct visible content structure. Steps 1–6 build that foundation; steps 7–8 are the recurring operation. Every step ships a real artifact you can verify.
Prompt → evidence → pipeline
Where we differ from an SEO/GEO agency.
Most agencies stop at the citation. We connect AI-search visibility to the operational infrastructure we build for automation clients, so a buyer's category question becomes a qualified, routed lead — not a contact-form submission.
prompt→pipeline · examplevisibility becomes pipeline
Measurement
How we measure observed AI visibility.
Mention coverage
/01Share of sampled prompts where the company appears.
Citation coverage
/02Share of sampled outputs where the company is linked or cited.
Competitor share
/03How often competitors appear across the same prompt set.
Factual accuracy
/04Whether AI systems describe the company, offer, geography, and expertise correctly.
Search eligibility
/05Indexing, crawlability, snippet eligibility, and structured-data validity.
Pipeline contribution
/06Qualified visits, CTA engagement, lead routing, and CRM outcomes.
AI answers vary by engine, model, location, language, account state, and time. Measurements are sampled observations, not guaranteed rankings.
Technical access
Crawler access is a technical check — not a guaranteed surface.
A sampled prompt set is monitored across selected AI search surfaces, locations, languages, and account contexts. For ChatGPT Search, Perplexity, and Bing we can verify the technical accessibility of individual crawlers, but access does not guarantee appearance in answers.
| Surface | Technical check |
|---|---|
| Google Search / AI features | Googlebot access, indexability, snippet eligibility |
| ChatGPT Search | OAI-SearchBot allowed where desired |
| Perplexity | PerplexityBot access where desired |
| Bing / Copilot | Bingbot access and Bing index health |
| Gemini · Claude · other engines | Sampled answer observation, not crawler-based ranking claims |
OpenAI: to be included in ChatGPT Search summaries, a site must not block OAI-SearchBot. Perplexity recommends allowing PerplexityBot to appear in Perplexity search. Google AI features rely on standard Googlebot crawling and indexing — Google-Extended controls Gemini app / model training use and does not affect Google Search or its AI features.
Engagement tiers
Three engagements, scoped to your market.
Foundation
Entry tierFor one service category and one primary market.
- Structured data
- Validated Organization, Service, Article markup
- Evidence architecture
- Priority page templates + 1 evidence pillar
- Surfaces observed
- Core engines, sampled prompt set
- Measurement
- Monthly observation
Category Authority
For competitive B2B categories, multiple services, and ongoing evidence creation.
- Structured data
- Validated markup across the priority library
- Evidence architecture
- Page templates + multiple pillars + comparisons
- Surfaces observed
- Core engines + broader prompt set
- Measurement
- Monthly observation + review
Multi-Market Visibility
For multilingual markets, multiple categories, regional variants, and recurring monitoring.
- Structured data
- Validated markup across markets & variants
- Evidence architecture
- Templates + pillars + regional evidence
- Surfaces observed
- Selected engines across locations & languages
- Measurement
- Recurring monitoring
We don't sell post volume. We build priority page templates, validated markup, evidence architecture, and ongoing measurement — scoped on a baseline audit, not on what we'd like to sell you.
Deliverables
What you receive — infrastructure, not post volume.
- Baseline AI-visibility audit across selected engines
- Prompt-market model for your category and markets
- Technical search-eligibility review (crawl, index, canonicals, internal links)
- Entity and evidence architecture across Organization, services, people, and proof
- Structured-data validation matched to visible content
- Priority page templates and evidence pages
- Sampled AI-surface monitoring and reporting
- CRM-connected conversion: capture, enrichment, and routing
Guardrails
What we do not do.
No fake mentions or synthetic reputation schemes
No hidden or misleading schema
No mass AI spam pages
No keyword stuffing for long-tail permutations
No llms.txt-as-magic positioning
No generic blog farms without first-hand evidence
No mass pages for every query variation
We publish what can be defended with visible evidence. See related pages: AI Workflow Automation, Enterprise RAG & Internal AI, AI Governance & Compliance, Case Studies, Insights.
The differentiator
How AI search visibility connects to automation.
Most AI/GEO agencies stop at the citation. They leave visibility traffic to land in a contact form and call it a day. Because Profitec AI is wired into automation, the visibility we earn for clients is connected to the same operational infrastructure we build for our automation engagements — lead intake, enrichment, CRM routing, follow-up sequences, and monthly reporting.
Stage 01 · Visibility layer
Become easier to surface
We improve how AI search systems discover, understand, and surface your pages for the prompts buyers actually use.
Stage 02 · Bridge layer
Capture the inbound
AI-driven traffic flows into enriched lead intake — not a generic contact form. Every prospect arrives with firmographic + behavioral context attached.
Stage 03 · Operations layer
Route, follow up, report
CRM routing, automated follow-up sequences, dashboards. The visibility becomes pipeline, not vanity metrics.
See our core automation services for how the operations layer is built — the same engineering team that ships the AIO/GEO foundation builds the CRM, reporting, and follow-up automations.
Timeline
Initial measurement window: 6–8 weeks.
Initial measurement window
6–8 weeks
First baseline, technical eligibility fixes, and entity/evidence work; first observations begin. Visibility timing depends on indexing, site condition, query type, market competition, language, and each platform's changing retrieval behavior.
Sustained operation
Ongoing
Evidence creation, sampled AI-surface observation, and conversion operations run monthly. Visibility is reported as sampled observations, not guaranteed rankings.
Week-by-week breakdown
W 1
Discovery + scope
Category, markets, target prompts, and surfaces confirmed.
W 2–3
Baseline + technical eligibility
Sampled prompts logged; crawl, index, canonicals, and internal links audited.
W 4–6
Entity + evidence + structured data
Entity graph clarified; priority page templates built; markup validated against visible content.
W 7–8
Observation + conversion
Sampled AI-surface observation begins; conversion capture and CRM routing wired.
W 9+
Recurring operation
Monthly observation, evidence creation, reporting, and conversion tuning.
Common questions
What people ask before we start.
01Is this just SEO with a new name?
For Google AI Overviews and AI Mode, foundational SEO remains the base: indexing eligibility, crawlability, snippet readiness, quality content, internal links, and strong page experience. What we add is an AI-visibility operating layer on top of that foundation: prompt-level monitoring, entity clarity improvements, structured evidence, and conversion integration into CRM workflows.
02Will my Google rankings be affected?
Usually the work supports organic performance because it improves technical quality, information architecture, and content clarity. We don't treat AI visibility as a replacement for SEO; we treat it as SEO-grade infrastructure extended to AI search surfaces.
03How do you measure success?
We track citation rate, mention rate, and competitor citation share across a stable prompt set, then connect those visibility signals to lead flow, CRM routing, and follow-up outcomes. Visibility metrics are useful, but the commercial metric is whether visibility becomes qualified pipeline.
04Can you do this for non-English markets?
Yes — and the Fershteyn case study shows it's often where the biggest opportunity sits. Russian-language estate planning queries had ~80% less competition than English. We've seen the same asymmetry in Spanish and Portuguese markets. Bilingual authority pages are usually the highest-leverage move.
05Do we need llms.txt to appear in Google AI Overviews?
No. Google does not require llms.txt or special AI text files for AI Overviews/AI Mode visibility. We treat llms.txt as an optional supporting asset for non-Google systems and internal documentation, not as a Google ranking prerequisite.
06Is Profitec AI an AI/GEO agency or an AI automation company?
Profitec AI is an AI automation company. AIO and GEO are one service vertical inside that broader automation and growth infrastructure model. We connect AI search visibility with CRM automation, lead handling, reporting, follow-up systems, and internal process automation so visibility and operations run as one engine.
07Does Profitec AI provide AI/GEO services globally?
Yes. We provide AI search visibility services for B2B companies in Israel, North America, Europe, and the UK. The work includes AI visibility audits, technical SEO and crawlability checks, structured-data validation, entity optimization, structured content, citation tracking, and competitor monitoring across ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI Overviews. Bilingual coverage (English + Russian) is available where it materially improves outcomes.
08How do we get started?
A 30-minute fit call. We discuss your practice, the queries that drive consultations, and where you currently stand. If we both want to move forward, the Foundation tier is usually the right starting shape — fixed setup, fixed monthly, and the audit ships within the first three weeks.
Next step
Want to see where you stand?
We'll run a baseline AI-visibility audit across selected engines for your category, and report exactly what your cited competitors have that you don't — and where your technical eligibility and evidence gaps are. 15-minute fit call to scope it.