AI Search Fixer by VisibilityPulse
AI Search Fixer
by VisibilityPulse
AI SCAN
AI SEARCH FIXERby VisibilityPulse

Get Cited in AI Search Answers. Before Your Competitors Do.

When prospective buyers research solutions in ChatGPT, Perplexity, or Claude, generative models synthesize answers from top-ranked vector sources. Engineered by VisibilityPulse, AI Search Fixer audits crawler blockades, 512-token RAG noise, and unlinked entities across 11 frontier engines in 120ms.

Target Engine:
Quick audit:
Coverage
11 Frontier Bots
100% Scored
Latency
Sub-120ms
Edge V8 Audit
ASF GEO
+41.5% Uplift
Citation Density
Access
100% Free
Zero Login Barrier
neural-brand-shield-v4
12ms
Citation Score
98.4%+4.2%
Vector RAG SNR
91.2%512 Tok
Bot Firewall
11/11 OK
Photorealistic 3D Neural Ingestion Core
Live Diagnostic StreamEvent #1 / 5
[10:14:28]OAI-SearchBot:GET /robots.txt
200 OK
SPECIALIZED DIAGNOSTIC ENGINES

The Generative Search Diagnostic Suite.

Every layer of the AI search stack audited deterministically: crawler accessibility, knowledge graphs, RAG token noise, and edge latency.

Launch Full Auditor
11 AI Crawlers

Frontier Engine Matrix

RFC 9309 rules, verified IP ranges, and citation vs training bot classification for OAI-SearchBot, PerplexityBot, and Claude-SearchBot.

Metric:100% RFC 9309 Strict
Live Vector RAG

Generative Citation Simulator

Simulate synthetic answer generation in ChatGPT Search and Perplexity Sonar. Test how your brand appears in synthetic footnotes.

Metric:Zero Hallucination
Head-to-Head

Competitive SOV Analyzer

Measure your generative Share of Voice (SOV) against competitors and uncover vector knowledge gaps in AI answer clusters.

Metric:Dual-Domain Benchmark
Wikidata Grounding

Schema Entity Graph

Validate JSON-LD @graph topology and link canonical Wikidata QIDs to anchor your brand identity in LLM knowledge graphs.

Metric:W3C & Schema.org v28
/llms.txt Standard

512-Token RAG Inspector

Analyze signal-to-noise ratio in 512-token chunks and audit your root /llms.txt markdown context for embedding models.

Metric:Dense Vector SNR
Anti-Bot Bypass

WAF Defense & Bot Radar

Detect silent AI crawler shadowbans caused by Cloudflare Bot Fight Mode or Akamai challenges that drop real-time citations.

Metric:Sub-120ms Latency
DETERMINISTIC SCORING SPECIFICATION

How AI Search Scores Are Calculated.

Unlike legacy tools that rely on subjective heuristics or probabilistic LLM hallucinations, AI Search Fixer calculates your Composite Generative Index (0–100) using a strict mathematical linear combination across 5 mission-critical pillars and 18 deterministic diagnostic modules.

S = 0.25·P_crawlers + 0.20·P_entity + 0.20·P_schema + 0.20·P_tech + 0.15·P_llms
Read Full Mathematical MethodologyInteractive Score Calculator ➔
25%
Crawler Access

11 frontier bots evaluated with strict RFC 9309 logic.

20%
Entity Grounding

Wikidata QIDs & Wikipedia canonical linkage.

20%
Schema Graph

Syntactic JSON-LD & interconnected @graph topology.

20%
Edge & WAF

Sub-120ms TTFB and zero anti-bot challenges.

15%
Semantic Context (/llms.txt)

High-SNR markdown designed for generative RAG contexts.

The Generative Shift

Built for Synthetic Citations. Not Static Keywords.

Traditional SEO relies on keyword matching in static inverted indexes. Frontier AI answer engines synthesize citations on-the-fly using vector embeddings and semantic entity graphs.

Legacy Paradigm

Static Keyword Indexing

Websites crawled once every few weeks, ranked by backlink counts and exact keyword density. No answer synthesis.

-User query matched against static inverted index
-Keyword density and backlink count determine rank
-Output: 10 blue links, no synthesis, no context
-Citations blocked by legacy robots.txt entries
-No entity disambiguation in Wikidata knowledge graph
-Slow TTFB causes AI agent request timeouts
Generative Frontier

Dynamic Citation Topology

Synthetic answer engines evaluate real-time vector embeddings, verify bot permissions, and cross-reference knowledge graphs to cite verified sources.

+Conversational query routed through semantic RAG ingestion
+Real-time bot handshake: OAI, Perplexity, Claude, Gemini
+Wikidata QID entity grounding eliminates hallucination risk
+ASF GEO direct answer density evaluated
+JSON-LD Schema.org @graph structured data verified
+Output: Primary cited source in AI synthesized answer
AUTHORITATIVE ENGINEERING MANUAL

The AEO & GEO Guide Book.

A complete 7-chapter technical handbook covering AI retrieval pipelines, RFC 9309 rules, ASF GEO heuristics, and copyable production configs.

Read Full Guide Book
CHAPTER 01-02

Anatomy & Protocols

Inverted index vs RAG vector retrieval, RFC 9309 rules, and citation vs scraper bot classification.

CHAPTER 03-04

Entity & ASF GEO

Wikidata QID grounding and the 9 ASF GEO heuristics yielding +41.5% citation uplift.

CHAPTER 05-06

RAG & Edge Latency

512-token semantic chunking, /llms.txt standard, and Cloudflare WAF bypass transform rules.

CHAPTER 07

Turnkey Playbook

A 48-hour step-by-step implementation sprint with copyable configs for robots.txt, schema, and edge workers.

Empirical Grounding

Deterministic Telemetry. Zero Assumptions.

11
Generative Engines Scored
GPTBot, PerplexityBot, ClaudeBot, DeepSeek, Applebot
+41%
ASF GEO Citation Lift
Academic benchmark boost from structured source density (arXiv:2311.09735)
100%
Deterministic Accuracy
Verified via live HTTP, Wikidata SPARQL, and Wikipedia APIs
$0.00
Zero-Cost Architecture
Runs on Cloudflare free tier and public-domain APIs only
Common Questions

Frequently Asked Questions

Everything you need to know about generative citations, crawler permissions, and machine discovery.

Traditional SEO optimizes for 10 blue links on search engine results pages. Answer Engine Optimization (AEO) focuses on structuring content so answer engines extract direct, concise paragraph snippets. Generative Engine Optimization (GEO) targets AI models that synthesize multi-source answers, requiring machine-readable facts, entity knowledge graphs, and crawler permissions.