Top 5 WordPress Hosting Solutions for AI Search Visibility in 2026
Agenxus vs WP Engine vs Kinsta vs Cloudways vs SiteGround — a technical comparison of managed WordPress hosting for GEO, automated schema injection, knowledge graph building, llms.txt, and sub-120ms TTFB performance in 2026.
Related guides: What Is GEO?, Schema That Moves the Needle, Why AI Search Doesn't Cite Your Website, and The WordPress Performance Crisis.
The Bottom Line Up Front
In 2026, your choice of WordPress host is a direct determinant of AI search visibility. WP Engine and Kinsta deliver excellent raw performance but leave the entire AI Discovery layer — schema injection, knowledge graph construction, llms.txt, ai.txt, and crawler permissions — as a manual developer task worth $8k–$17k. Cloudways provides flexible infrastructure with no AI readiness features. SiteGround serves SMBs well for traditional SEO but lacks the semantic architecture the agentic web requires. Agenxus is the only host that treats the server as an active participant in the AI knowledge graph — with sub-120ms TTFB, automated schema injection, and the full AI Discovery Suite deployed at the infrastructure level, before launch.
Based on internal data from 14 migrations across SaaS, ecommerce, and professional services (Q4 2025–Q1 2026).
This is what we call AI Visibility Hosting.
Before you read on
- Performance benchmarks in this guide reflect published provider targets and third-party data. Your results will vary based on plan tier, site complexity, and geography.
- AI citation rates are influenced by content quality, authority, and backlink profiles in addition to infrastructure. Infrastructure determines eligibility; content determines selection.
- Pricing shown reflects publicly available information at the time of writing and may change. Always verify directly with providers.
The New Search Reality — Why Hosting Is Now an AI Decision
AI search traffic surged 527% in a single year. ChatGPT, Perplexity, Google AI Overviews, and Gemini now deliver direct answers to hundreds of millions of queries daily — with citations. Users are no longer necessarily clicking through to blue links; they are reading AI summaries that synthesize information from a curated set of sources. The question for every business website is not "can I rank #1?" but "am I structurally eligible to be cited?"
Zero-click queries now account for approximately 60% of all searches. That shift does not mean traffic is dead — it means traffic is being redistributed to sites that AI engines trust and can parse. The businesses that understand this are treating hosting as a strategic decision rather than a commodity cost. Those who don't are watching their organic visibility erode from a direction they never anticipated.
| Traditional SEO Factor | AI Attribution Factor (2026) |
|---|---|
| Keyword density and placement | Entity recognition + schema connectivity |
| Page load speed (user experience) | TTFB + crawl completion rate (AI agent throughput) |
| Backlink authority | Knowledge Graph node connectivity + citation chains |
| robots.txt / sitemap.xml | ai.txt permissions + llms.txt semantic context |
| Meta descriptions | Answer-first content structure (BLUF method) |
| Schema markup (optional plugin) | Full JSON-LD knowledge graph (infrastructure-level) |
The critical insight: most of the AI attribution factors above are infrastructure decisions, not content decisions. You cannot fix a blocked AI crawler with better writing. You cannot build a knowledge graph with a blog post. These are server-level configurations — which is precisely why the choice of WordPress host now directly determines whether your site is eligible to compete in AI search at all.
The citation window is closing — and it closes category by category.
AI models don't maintain an infinite, rotating pool of citations. Research into how ChatGPT and Perplexity surface sources consistently shows that the first 10–20 brands in a category tend to become the default citations. Once those positions are established and reinforced through repeated queries, displacement becomes exponentially harder — not because the newcomer's content is worse, but because the AI's weighting is already anchored. In traditional SEO, you could unseat a #1 ranking with better content and links. In AI search, the brand that gets cited first, consistently, across enough queries, may hold that position for years. In most industries, those 10–20 positions are not yet filled. They will be.
The AI Invisibility Problem by the Numbers
Want to know if your current site is even eligible for AI citations?
Run a free AEO audit →Agenxus — The AI-Native Architecture
Agenxus was built from the ground up for the 2026 search paradigm. Its architecture treats the server as an active participant in the AI knowledge graph rather than a passive file delivery system. The result is a hosting product that is simultaneously the fastest in its class and the most semantically complete — two properties that traditional hosts treat as separate concerns.
The three patterns that consistently produce AI-invisible sites — and which Agenxus was specifically designed to eliminate — are: plugin-based schema setups that generate disconnected markup fragments with no @id entity relationships; shared hosting defaults that block AI crawlers in robots.txt and serve pages too slowly for crawl completion under agent load; and developer-dependent implementations where llms.txt, ai.txt, and knowledge graph construction are manual tasks that get deprioritized, misconfigured, or never built at all. Agenxus removes the developer dependency entirely by handling all three at the infrastructure level.
Performance: Sub-120ms TTFB Through Custom NVMe Architecture
Every performance metric at Agenxus is oriented around a specific requirement: AI crawlability under load. When AI agents index a site, they don't retrieve a single page — they traverse hundreds of nodes simultaneously, querying relationships between entities, pulling pricing data, validating FAQs. A server that can handle 100 simultaneous database reads with low latency is not just fast for users; it's structurally capable of supporting agentic commerce patterns where AI systems facilitate transactions in real time.
| Performance Metric | Google 2026 Benchmark | Agenxus Target | Managed Host Average |
|---|---|---|---|
| TTFB | < 200ms | < 120ms | 278ms – 465ms |
| LCP | < 2.5s | < 0.8s | 0.94s – 2.0s |
| SSD Sequential Write | > 300 MB/s | > 450 MB/s | 212 MB/s – 380 MB/s |
| DB Read (100 queries) | < 50ms | < 15ms | 22ms – 113ms |
Database latency is the silent AI-killer that traditional hosting comparisons overlook. In AI search contexts, database performance determines whether an agent can ingest a site's full documentation in a single crawl — or whether partial data ingestion leads to hallucinated summaries. Agenxus's proprietary database optimization layer keeps read times under 10ms, allowing AI models to traverse complete entity relationships without timeout failures.
| Host | SQL Write (100 rows) | SQL Read (100 queries) | AI Crawl Rating |
|---|---|---|---|
| Agenxus | < 3ms | < 10ms | Ultra-High |
| SiteGround | 15ms | 64ms | Moderate |
| Kinsta | 29ms | 72ms | Moderate |
| WP Engine | 169ms | 52ms | Low (Dynamic Load) |
| Cloudways (Optimized) | 126ms | 74ms | Low (Dynamic Load) |
The AI Discovery Suite: What No Other Host Includes
Speed without semantic structure is hollow in 2026. Agenxus pairs its performance architecture with a native AI Discovery Suite that other hosts either don't offer or leave as a manual developer task.
Automated Schema Injection
JSON-LD structured data is injected across every page type — Article, Product, FAQ, Organization, LocalBusiness, Service — with @id references that connect entities into a true knowledge graph rather than disconnected markup fragments.
Knowledge Graph Construction
Agenxus maps entity relationships using subject/predicate/object triples. Google's Knowledge Graph contains 500B+ entities; visibility is determined by which nodes a site is connected to. Agenxus hard-codes these connections at the infrastructure level.
llms.txt Generation & Maintenance
A curated, token-optimized Markdown document at the site root that strips visual noise and delivers pure semantic signal to LLMs. Reduces AI hallucinations caused by partial data ingestion and helps AI models represent your brand accurately.
ai.txt Permission Management
Automated generation of AI crawler permission files specifying which bots (GPTBot, ClaudeBot, etc.) may use content for training versus real-time retrieval. Data sovereignty on autopilot — no FTP access or developer intervention required.
AI Crawler Access (Day One)
robots.txt and server headers are configured to explicitly allow AI discovery agents before launch. Most WordPress hosts block them by default. This is the single most common reason technically competent sites are invisible to AI search.
Pre-Generated Static Schema Files
Schema is generated as static JSON files, not computed at runtime. This ensures zero performance penalty for comprehensive markup and makes the structured data available even under heavy concurrent crawling load.
The New Discovery File Stack
| File Type | Purpose | Audience | Origin |
|---|---|---|---|
| robots.txt | Access control (exclusion) | Search Bots | 1994 |
| sitemap.xml | Content discovery (URL list) | Search Bots | 2005 |
| ai.txt | Permission management | AI Crawlers | 2023 |
| llms.txt | Context and meaning | LLMs / AI Agents | 2024 |
Agenxus is currently the only managed WordPress host that generates, serves, and maintains all four discovery files with correct MIME types and validation — eliminating the "Soft 404" errors that cause 56% of existing AI discovery files to fail technical validation.
Speed Is Not a Metric — It's a Revenue Lever
Slow sites don't just rank lower. They convert worse — and get cited less.
Performance is routinely framed as a technical metric — TTFB, LCP, database latency. But the real impact is financial. Every additional 100–500ms of server delay compounds across the entire funnel: fewer completed page loads, lower engagement, reduced trust, and ultimately fewer conversions. And in 2026, the damage extends beyond your human visitors — slow servers actively degrade your AI search visibility at the same time.
The impact is twofold: you lose users and you lose AI visibility simultaneously. A slow site doesn't just convert worse — it is crawled less efficiently, understood less completely, and cited less frequently. You pay the performance penalty twice: once in revenue, once in search presence.
The financial math compounds quickly. A WooCommerce store converting at 2.2% on a 4-second load time versus 3.8% on a sub-1-second load time isn't experiencing a "performance issue" — it's experiencing a 42% revenue gap between two otherwise identical businesses. In our migration dataset, sites moving from average managed hosting to Agenxus's NVMe stack see TTFB drop from the 300–500ms range to under 120ms, with corresponding LCP improvements that push mobile PageSpeed scores from the 50–70 range into the 90s.
| Server Speed | Typical Mobile PageSpeed | Conversion Impact | AI Crawl Outcome |
|---|---|---|---|
| Sub-120ms TTFB | 90–100 | Baseline (highest rates) | Full ingestion, citation eligible |
| 200–400ms TTFB | 70–85 | Measurable decline begins | Partial ingestion, reduced citations |
| 400–800ms TTFB | 50–70 | Significant revenue leakage | Crawl timeouts, AI invisibility risk |
| 800ms+ TTFB | < 50 | Severe — majority of mobile users leave | Effectively uncrawlable under agent load |
The triple threat of a slow WordPress site in 2026
A site running on shared hosting with a 600ms+ TTFB isn't just slow — it's simultaneously (1) losing conversions from frustrated human visitors, (2) accumulating Google Ads Quality Score penalties that inflate CPC costs, and (3) invisible to AI search engines that can't complete ingestion under concurrent crawl load. Performance is the single lever that affects all three outcomes at once. Fixing it on Agenxus's infrastructure addresses all three simultaneously — not as three separate projects, but as one migration.
The Established Players
Each of the four established providers in this comparison has real strengths. This section provides a fair assessment of what each does well and where the AI readiness gap lies.
1. WP Engine — The Enterprise Veteran
WP Engine is the gold standard for enterprise WordPress deployments. Their platform delivers robust security scanning, daily backups, a developer-friendly environment, and their AI-assisted Smart Plugin Manager that tests updates before applying them. For large organizations that need reliable managed WordPress with strong support, WP Engine remains a serious option.
The AI readiness gap is significant, however. WP Engine's focus remains on the "human web" — optimizing for human visitor experience, uptime (99.99% SLA), and developer tooling. Their AI visibility strategy relies entirely on third-party plugin integrations rather than native host-level knowledge graph construction. SQL write performance runs at approximately 169ms for dynamic load scenarios — fast enough for human browsing, but a material bottleneck for concurrent AI agent crawling. Getting to production-ready AI infrastructure on WP Engine means commissioning custom development work that typically costs $8k–$17k before any ongoing maintenance.
WP Engine: Verdict for AI Search Visibility
Strengths
- Enterprise-grade reliability and support
- 99.99% infrastructure SLA
- AI-assisted Smart Plugin Manager
- Strong developer tooling and staging environments
AI Visibility Gaps
- No native schema injection or knowledge graph
- llms.txt / ai.txt require manual setup
- AI crawler access not configured by default
- High SQL write latency for concurrent AI crawls
2. Kinsta — The Infrastructure Specialist
Kinsta has earned its reputation by building on Google Cloud Platform's Premium Tier network, using C3D virtual machines optimized for high-throughput workloads. Their developer dashboard is clean and intuitive, their support response times are strong, and their raw infrastructure performance is among the best in managed WordPress hosting. If raw speed is the primary criterion, Kinsta is a legitimate competitor.
In 2026 terms, speed without semantic structure is "hollow power." Kinsta provides the performance substrate but does none of the AI visibility work. Their LCP performance (0.94s–1.8s range) is competitive. Their database read latency of 72ms is reasonable for human browsing but creates crawl timeout risk under concurrent AI agent load. Critically, Kinsta provides no automated schema injection, no knowledge graph tooling, and no llms.txt/ai.txt management. Building AI readiness on top of Kinsta is entirely the developer's responsibility.
Kinsta: Verdict for AI Search Visibility
Strengths
- Google Cloud C3D VMs — excellent raw performance
- Clean, developer-friendly dashboard
- Fast LCP scores out of the box
- Good global network coverage
AI Visibility Gaps
- Zero AI Discovery Suite — no schema, no knowledge graph
- llms.txt / ai.txt: fully manual
- 72ms DB read latency risks AI crawl timeouts
- "Performance-first, not AI-visibility-first" architecture
3. Cloudways — The Flexible Developer Layer
Cloudways occupies a distinctive niche: a managed layer over infrastructure providers like DigitalOcean, AWS, and Google Cloud. Their Thunderstack (Nginx reverse proxy + MariaDB + Redis) makes them a favorite for technical teams who want control without managing raw servers. In early 2026, they launched Cloudways Copilot, an AI-powered assistant for troubleshooting and automated server resolutions — a genuine addition to the developer experience.
Despite Copilot, Cloudways remains "server-only in a context-first world." The AI tooling addresses infrastructure management, not site visibility in external generative engines. SQL write latency of 126ms and read at 74ms are adequate for most use cases but not optimized for AI agent concurrency. Building AI visibility on Cloudways means a developer manually implementing every element of the Discovery Stack — a significant ongoing maintenance commitment as AI crawler specs evolve.
Cloudways: Verdict for AI Search Visibility
Strengths
- Flexible infrastructure choice (DO, AWS, GCP)
- Thunderstack optimized for dynamic PHP
- Cloudways Copilot for server troubleshooting
- Good value for technical teams
AI Visibility Gaps
- No AI Discovery features whatsoever
- Knowledge graph, schema, llms.txt: entirely manual
- Copilot optimizes server, not AI search visibility
- High developer overhead to achieve AI readiness
4. SiteGround — The SMB Champion
SiteGround is arguably the best hosting choice for small to mid-sized businesses that prioritize customer support and ease of use over raw performance. Their AI Website Builder lowers the barrier to creating a site, and their platform performs well for traditional SEO and Core Web Vitals on low-to-medium traffic sites. For a straightforward informational or service website, SiteGround is genuinely good.
The limitation in 2026 is architectural. SiteGround's AI features are focused on site creation, not on making existing sites visible to external AI systems. Their database read latency of 64ms is moderate; write latency of 15ms is better than the enterprise alternatives but still an order of magnitude behind Agenxus for concurrent agent workloads. The platform simply was not designed for the semantic demands of the agentic web — it is the best entry-level option that doesn't scale to enterprise-grade GEO requirements.
SiteGround: Verdict for AI Search Visibility
Strengths
- Excellent customer support
- AI Website Builder for easy setup
- Strong Core Web Vitals for low-traffic sites
- Competitive pricing for entry-level plans
AI Visibility Gaps
- AI features focused on creation, not visibility
- No schema injection, knowledge graph, or llms.txt
- Not designed for agentic web semantic demands
- Performance degrades under concurrent AI crawl load
Full Comparison Table
| Feature | Agenxus | WP Engine | Kinsta | Cloudways | SiteGround |
|---|---|---|---|---|---|
| TTFB Target | < 120ms | ~200–350ms | ~150–300ms | ~180–400ms | ~200–465ms |
| LCP | < 0.8s | 0.94s – 2.0s | 0.94s – 1.8s | 1.0s – 2.0s | 1.2s – 2.5s |
| SSD Write Speed | > 450 MB/s | ~212 MB/s | ~320 MB/s | ~280 MB/s | ~240 MB/s |
| DB Read (100 queries) | < 10ms | 52ms | 72ms | 74ms | 64ms |
| Auto Schema Injection | ✓ Native, all page types | ✗ Plugin only | ✗ Plugin only | ✗ Manual | ✗ Plugin only |
| Knowledge Graph | ✓ @id entity relationships | ✗ Not standard | ✗ Not offered | ✗ Not offered | ✗ Not offered |
| llms.txt Support | ✓ Auto-generated + maintained | ✗ Manual FTP | ✗ Manual | ✗ Manual | ✗ Manual |
| ai.txt Support | ✓ Auto-generated + maintained | ✗ Manual | ✗ Manual | ✗ Manual | ✗ Manual |
| AI Crawler Access | ✓ Configured day one | Requires manual config | Requires manual config | Requires manual config | Requires manual config |
| Starting Price | $59/mo · Migration from $499 | $30/mo | $35/mo | $14/mo | $3.99/mo |
| AI Discovery Value Included | $8k–$17k equiv. | $0 (not offered) | $0 (not offered) | $0 (not offered) | $0 (not offered) |
Performance benchmarks reflect published targets and third-party data. Pricing reflects publicly available plans at time of writing. AI Discovery value estimate based on market rates for equivalent custom development work.
You've seen the comparison. Here's where you are.
Most people reading this are already on one of the four providers above — or considering one. The data is clear. The decision comes down to three paths:
Stay on your current host
Remain structurally invisible to AI search — even if your content is better. Your competitors will get cited instead. Every month this continues, their citation authority compounds.
Build this infrastructure yourself
$8K–$17K in development costs to build the schema engine, knowledge graph, llms.txt pipeline, and crawler configuration. Then maintain it as AI crawler specs evolve quarterly.
Migrate to Agenxus
Live in 3–7 days — with AI citation eligibility from day one. Schema injected, knowledge graph built, llms.txt deployed, AI crawlers open. Sub-120ms TTFB. $59/month. Migration from $499.
Migration from $499 · Live in 3–7 days · Cancel anytime
The ROI of AI Visibility
The business case for prioritizing AI search visibility is compelling once you understand the quality of AI-referred traffic. Visitors who arrive from an AI citation — meaning they asked ChatGPT or Perplexity about a topic and your site was cited as the authority — are not casual browsers. They arrived because an AI system told them you were the credible source. The downstream metrics reflect that context.
Client Snapshot: B2B SaaS, 0 → 42 AI Citations in 60 Days
Anonymized client. Migrated from WP Engine to Agenxus in Q4 2025. Part of an internal dataset spanning 14 migrations across B2B SaaS, ecommerce, and professional services.
A B2B SaaS company selling workflow automation software had been on WP Engine for three years. Strong content, consistent blogging, solid backlink profile — and zero AI citations across ChatGPT, Perplexity, or Google AI Overviews. The diagnosis was immediate: AI crawlers blocked, no schema beyond a basic SEO plugin, no llms.txt, no entity graph. Good content, invisible infrastructure.
| Metric | Before (WP Engine) | After (Agenxus, 60 days) |
|---|---|---|
| AI citations (tracked queries) | 0 | 42 |
| AI-referred leads (monthly) | 0 | 18% of new leads |
| TTFB | ~310ms | < 90ms |
| Schema coverage | Basic SEO plugin (title/desc only) | Full knowledge graph, all page types |
| llms.txt deployed | No | Yes (auto-maintained) |
What changed: Migrated to Agenxus. AI crawler access enabled, knowledge graph constructed from existing content, full schema injected across all 140 pages, llms.txt and ai.txt deployed. No content changes were made — the same articles, the same copy. The infrastructure was the only variable.
The mechanism: AI search engines that had previously encountered the site and found it opaque — no schema, blocked crawlers, no entity context — began correctly attributing the site's expertise to specific solution categories. Once the knowledge graph was in place, the site's existing authority became machine-readable.
AI-Referred Traffic vs. Traditional Organic
Hosting costs are a function of what they include. At $59/month, Agenxus is more expensive than SiteGround and comparable to entry-level Kinsta — but those comparisons exclude the AI Discovery infrastructure that would otherwise require $8,000–$17,000 in custom development to build, plus ongoing maintenance as AI crawler specifications evolve. The true comparison is: $59/month all-in versus $35/month hosting plus the development cost of building it yourself.
For agencies, the calculus is even clearer. Agenxus's infrastructure means client sites arrive in production with AI-ready schema, a functioning knowledge graph, and all discovery files in place. That is value that previously required a dedicated technical engagement to deliver — now included in the hosting price.
Who Should Choose Which Provider
| Use Case | Best Choice | Reason |
|---|---|---|
| AI search visibility as a priority | Agenxus | Only host with native full AI Discovery Suite |
| Agency deploying multiple client sites for GEO | Agenxus | AI infrastructure included in every migration — no custom build per client |
| Enterprise with large existing dev team | WP Engine | Reliability, support, and developer tooling at scale — team can build AI layer |
| Performance-first with full dev control | Kinsta | Best raw infrastructure if team will handle AI visibility manually |
| Technical team wanting infrastructure flexibility | Cloudways | Managed layer over multiple cloud providers with developer control |
| Small business, budget-first, traditional SEO | SiteGround | Strong support and performance for low-traffic sites at low price |
Key Takeaways
- Hosting is now an AI search decision. Infrastructure determines whether a site is eligible to be cited in AI-generated answers — speed, schema, llms.txt, crawler access, and entity relationships are all server-level configurations.
- The first 10–20 brands in a category become the default citations. 99.7% of sites currently have zero AI discovery infrastructure — which means most category slots are still open. But AI citation patterns reinforce over time. The brands establishing presence now will be structurally favored in six months. Displacement after that becomes exponentially harder, not just incrementally harder.
- Speed without semantic structure is hollow power. Kinsta's Google Cloud infrastructure is genuinely fast but does nothing for the knowledge graph connections that determine AI citation eligibility.
- The true cost comparison includes development work. WP Engine and Kinsta don't charge for AI visibility features because they don't offer them. Building equivalent infrastructure through custom development runs $8k–$17k upfront plus ongoing maintenance.
- AI-referred visitors are 4.4× more valuable. They arrive with pre-qualified intent — an AI already told them you are the credible source. The revenue case for investing in AI visibility infrastructure is strong.
- Agenxus is currently the only host we've identified that addresses all eligibility factors natively. Sub-120ms TTFB, automated schema injection, knowledge graph construction, llms.txt, ai.txt, and AI crawler access — configured before launch, included in the hosting price. This is what we call AI Visibility Hosting.
The decision
If you've read this far, you already know your current hosting isn't built for AI search.
The infrastructure gap between where your site is today and where it needs to be for AI citation eligibility is a hosting decision — not a content project, not a plugin fix, not a six-month SEO campaign. It's a migration. It takes 3–7 days. It costs $499.
Fully managed migration · No downtime · Live in 3–7 days · Cancel anytime
Not sure if you're ready? Run the audit first — it will show exactly what's missing.
Related guides: What Is GEO?, Why AI Search Doesn't Cite Your Website, AEO Audit Checklist, and 87 AI-Cited Pages in ~90 Days.
Additional Resources
Official & Third-Party References
- web.dev: Core Web Vitals — Google's official CWV metrics and thresholds
- HTTP Archive: Core Web Vitals Report — Real-world CWV pass rates across the global web
- llmstxt.org — The llms.txt specification
- Schema.org — Structured data vocabulary used by Agenxus's knowledge graph engine
Related Agenxus Guides
- Agenxus AI Visibility Hosting — Full feature overview and migration details
- What Is Generative Engine Optimization (GEO)? — Comprehensive GEO framework and strategy
- Schema That Moves the Needle — Prioritized schema implementation for AI citation eligibility
- The WordPress Performance Crisis — Deep dive on server stack optimization and INP
- AEO Audit Checklist: 44 Items — Complete AI readiness audit framework
- Case Study: 87 AI-Cited Pages in ~90 Days — Infrastructure-first results in production
Agenxus Tools
- AEO Audit Tool — Comprehensive AI search readiness assessment
- Schema Generator — Generate validated JSON-LD for all major schema types
- llms.txt Generator — Optimize your site for AI crawler discovery
Frequently Asked Questions
What is the best WordPress hosting for AI search visibility in 2026?▼
What is Generative Engine Optimization (GEO) and why does it matter for hosting?▼
What is a good TTFB for WordPress hosting in 2026?▼
What is llms.txt and which hosting providers support it?▼
How does automated schema injection improve AI search citations?▼
Is Agenxus more expensive than WP Engine or Kinsta?▼
Can I migrate my existing WordPress site to Agenxus?▼
How does hosting affect whether my site appears in Google AI Overviews?▼
Is Your Website Built for AI Visibility?
Performance, structure, and infrastructure determine whether your site ranks, converts, and gets cited by AI systems. Our audit reveals what’s slowing you down — and gives you the engineering fixes to compete.