AI Discoverability
The single most overlooked competitive advantage in B2B marketing in 2026 — and kastle.com isn’t optimized for AI discovery.
How AI Search Actually Works
When a buyer asks ChatGPT, Perplexity, or Google's AI Overviews about managed physical security providers, the AI system:
Crawls and indexes the web using its training data and real-time retrieval
Cites sources it deems authoritative, specific, and semantically complete
Returns an answer — typically naming 3–5 vendors with brief descriptions
The buyer never clicks Google — they read the AI summary and either contact the named vendor or refine their question
This is the channel where Kastle needs to be found. And the factors that determine AI citability are specific, measurable, and — unlike traditional SEO — achievable in weeks, not years.
Kastle's Current AI Visibility — An Honest Assessment
Methodology
AgencyQ audited kastle.com against the 12 primary factors that determine AI search citability — drawn from analysis of how ChatGPT (77.97% of AI search traffic), Perplexity (15.10%), and Google AI Overviews select and cite sources. Unlike the NACUBO engagement, kastle.com is not blocked from indexing — but there are significant structural gaps that prevent AI systems from extracting, summarizing, and citing Kastle's content effectively.
| Factor | Current State | Proposed State |
|---|---|---|
Basic Indexing | ✓ Indexed — no noindex tags | ✓ Maintained + enhanced sitemap |
Organization Schema | ✓ Present — name, logo, social profiles | ✓ Enhanced — contact info, locations, service areas |
Product/Service Schema | ✗ Missing — no schema on Access Control, VideoGuarding, Visitor Mgmt pages | ✓ Full Product schema on all 8+ solution pages with pricing, features, areas served |
FAQ Schema | ✗ Missing — no FAQ markup on any solution page | ✓ 5–8 FAQ entries per solution page in structured Q&A format — directly citable by AI |
Article/Blog Schema | ✗ Missing — 80+ resource articles with no Article schema | ✓ Article schema on all resource hub content — author, datePublished, topic category |
LocalBusiness Schema | ✗ Missing — locations listed as plain text only | ✓ LocalBusiness schema for all regional offices — coordinates, service areas, hours |
Open Graph Tags | ✗ Missing from interior pages — no og:image, og:title on solution/resource pages | ✓ Complete OG suite on all pages — enables preview cards in social + AI chat sharing |
Semantic Content Structure | ✗ Feature-led copy with no Q&A format — AI cannot extract direct answers | ✓ Outcome-first copy + inline FAQ sections — structured for AI answer extraction |
llms.txt | ✗ Not present — no AI-specific discovery guidance | ✓ llms.txt created — guides AI crawlers to highest-value content and use cases |
AI-Citeable Content Depth | — Variable — some resource articles are 1,500+ words, most solution pages are thin | ✓ 2,900+ word pillar pages per core topic — structured for AI citation patterns |
Speakable Schema | ✗ Not implemented — content not marked as AI/voice-readable priority | ✓ Speakable markup on key summary sections — enables voice and AI assistant prioritization |
robots.txt AI Rules | — Standard — no explicit allow/deny for AI crawlers (GPTBot, ClaudeBot, PerplexityBot) | ✓ Explicit allow rules for AI bots — ensures training and retrieval access is unambiguous |
Five Moves That Make Kastle AI-Citable
8 Solution Pages: Feature Lists → AI-Answerable Summaries
Each solution page (Access Control, VideoGuarding, Visitor Management, etc.) gets restructured with a 150-word plain-language summary at the top — written specifically for AI extraction. When a buyer asks 'what does Kastle VideoGuarding do?', that summary becomes the citable answer. Five FAQ entries per page, in Q&A format, with specific numbers and outcomes.
↑ Expected Impact:
Direct citation in AI search results for brand + category queries
Product Schema on Every Solution Page
JSON-LD Product schema deployed across all solution offerings: Managed Access Control, VideoGuarding, Kastle EverPresence, Visitor Management, Parking Management. Each schema entry includes name, description, offers, serviceArea (US markets), and brand. AI systems weight structured data heavily when constructing vendor comparison answers.
↑ Expected Impact:
Kastle appears in structured AI comparisons alongside Verkada, Brivo, and Avigilon Alta
FAQ Schema on High-Intent Solution Pages
5–8 FAQ entries per solution page, marked up in JSON-LD FAQ schema: 'How long does managed access control installation take?' 'What is the difference between VideoGuarding and traditional CCTV monitoring?' 'Does Kastle work in multi-building portfolios?' These become direct AI Overview featured answers for the most commercially valuable queries in the physical security category.
↑ Expected Impact:
Featured snippet capture and direct AI answer for high-commercial-intent queries
LocalBusiness Schema for All Regional Offices
Convert the static Locations list page into 8–10 LocalBusiness schema entries, each with: name, address, geo-coordinates, serviceArea radius, telephone, and openingHours. A buyer who asks ChatGPT 'who handles managed security in Washington DC?' will find a citable, structured Kastle result rather than a blank.
↑ Expected Impact:
Regional AI search visibility for market-specific buyer queries
llms.txt + AI Crawler Explicit Access
Create an llms.txt file at kastle.com/llms.txt that guides AI crawlers to Kastle's most authoritative content: solution summaries, customer case studies, the Occupancy Barometer data. Update robots.txt to explicitly allow GPTBot, ClaudeBot, PerplexityBot, and GoogleBot-Extended. This ensures AI training and retrieval pipelines can access and index Kastle's best content without ambiguity.
↑ Expected Impact:
Consistent AI training inclusion and retrieval access for all major AI platforms
The Industry Evidence
50%
of B2B buyers now start their vendor research journey in an AI chatbot (ChatGPT, Perplexity, Gemini) rather than Google — a 71% increase in just four months.
Digital Commerce 360 / BusinessWire: 2025 B2B AI Search Study
89%
of B2B buyers use generative AI somewhere in their purchasing process. Among large enterprises, 42% now depend on AI specifically for vendor discovery.
Relixir: 2025 Enterprise Buyers AI Search Visibility Report
4.2×
more likely to be cited by AI systems with semantic completeness — structured data, Q&A format, specific facts, and clear entity markup.
GEO-16 Framework: AI Answer Engine Citation Behavior, arXiv 2509.10762
70%
of enterprise queries will shift to generative AI engines within two years, according to Gartner's 2025 projection. This is not an emerging trend — it is the present state of B2B buyer behavior.
Gartner: AI Search Survey 2025 / Conductor AEO/GEO Benchmarks 2026
What Happens When Kastle Owns AI Search for Physical Security
Today — Without AI Optimization
1.
Buyer asks ChatGPT: "Who are the top managed security providers for commercial real estate?"
2.
ChatGPT returns Verkada, Brivo, Avigilon Alta, Genetec — companies with product schema, FAQ markup, and AI-optimized content
3.
Kastle may appear — but without structured data, it cannot provide the specific, extractable facts AI systems prefer to cite
4.
Buyer contacts a competitor for a demo
5.
Kastle never knew the opportunity existed
Post-Implementation — AI-Optimized
1.
Buyer asks ChatGPT: "Who are the top managed security providers for commercial real estate?"
2.
ChatGPT finds Kastle's structured Product schema, FAQ answers, and 2,900-word CRE Security pillar page — all citable
3.
Kastle is cited with a specific, authoritative summary: "50+ years, 60,000+ businesses, fully managed model"
4.
Buyer visits kastle.com from the AI citation — a pre-qualified, high-intent lead
5.
New hero CTA, 5-field form, and attributed testimonials convert that visit into a demo request
The Timeline Advantage
Traditional SEO takes 6–12 months to move the needle. AI discoverability works faster: structured data is crawled and indexed by AI systems within days of deployment. FAQ schema and Product schema changes have been observed to influence AI citation behavior within 2–4 weeks. The companies that implement AI optimization now will establish category authority before competitors recognize the channel exists. In a market where 50% of buyers are starting in AI and that number is growing 71% every four months, first-mover advantage is measured in weeks.
Week 1–2
Product + FAQ schema deployed across all solution pages
Week 2–4
Article schema on resource hub + LocalBusiness schema for all offices
Week 4+
llms.txt live + pillar content published + AI citation monitoring active
Projected KPI Impact
Metric
Current State
Post-Implementation
AI-referred traffic from ChatGPT, Perplexity, and Google AI Overviews
Near-zero (unstructured content, no extractable summaries)
Measurable and growing — tracked via UTM and referrer analytics
Citation rate in AI responses to "managed physical security" queries
Inconsistent — dependent on training data depth, no structured signals
4.2× improvement in AI citability with full structured data implementation
Featured snippet capture for FAQ queries
None — no FAQ schema on any page
Target: 10+ featured snippet positions for high-commercial-intent queries
Regional AI search visibility
None — no LocalBusiness schema, no geo-specific structured signals
Coverage in AI responses to "managed security [city]" queries across all Kastle markets
AI-referred lead quality score
N/A
Expected to be highest-intent cohort — AI-referred visitors are 2–3 steps into their research before clicking
Sources: Digital Commerce 360 / BusinessWire 2025 B2B AI Search Study · Gartner Sales Survey 2025 · Relixir Enterprise AI Visibility Report 2025 · GEO-16 Framework (arXiv:2509.10762) · Conductor AEO/GEO Benchmarks 2026 · Google Search Central structured data documentation
From Invisible to Authoritative
The fix is Sprint 1 — and it's already specified
Live Demo
Pages → Homepage
The conversion and messaging layer is live; AI discoverability work is defined for Sprint 1 execution.
Business Case
Context → Business Case
Details the structured-data gap and ROI impact of fixing it.
Technical Approach
Context → Technical Approach
How structured data is implemented in this React/TypeScript stack.
Sprint Plan
Context → Sprint Plan & Roadmap
AI discoverability fixes ship in Sprint 1, Week 2.