AEOBoost
AI Search visibility platform for SEO consultants & agencies
AEOBoost.app: AI Search Visibility Platform for SEO Professionals
Built for independent SEO consultants, freelancers, and boutique agencies, AEOBoost helps you audit, fix, track, and prove client visibility across AI Search engines—including ChatGPT, Perplexity, Google AI / Gemini, and Claude.
Clients are no longer just asking about Google keyword rankings; they want to know why their brand is missing when buyers ask AI engines for recommendations. AEOBoost integrates AI Search visibility directly into your existing client SEO workflow: Audit → Fix → Track → Prove.
Key Capabilities:
Comprehensive 7-Pillar Audit: Scan any client or prospect URL in under 60 seconds across the critical signals AI engines evaluate: E-E-A-T, Schema JSON-LD, content freshness, semantic density, authoritative link references, crawler access (robots.txt), and brand entity signals.
Live Multi-Model Citation Testing: Query ChatGPT, Perplexity, Gemini, and Claude in real time on high-intent target queries. See whether your client is cited, discover which competitors AI recommends instead, and uncover exact topical gaps.
AI Crawler Access Inspection: Check whether GPTBot, PerplexityBot, or ClaudeBot are accidentally blocked or missing key page elements on client domains.
Before & After Progress Tracking: Re-scan client websites after implementing technical schema or content optimizations to measure the score delta and deliver clear proof of SEO progress.
Client Reports & 1-Click LLM Export: Generate presentation-ready executive PDFs, structured Markdown summaries, or instant "Copy for LLM" prompts ready to paste into your favorite AI tool.
Turn AI Search visibility into a reliable client retention and prospecting asset for your SEO business.
Features
Key Features
- Comprehensive 7-Pillar AEO Technical Audit: Scans any URL in under 60 seconds across the critical signals evaluated by LLMs:
- E-E-A-T Signals: Author bios, credentials, and organizational trust markers.
- Structured Data & Schema.org: JSON-LD validation (Organization, Product, FAQPage, etc.).
- Content Freshness & Recency: Publication dates, update frequency, and timestamp markers.
- Semantic Density & Conversational Fit: Natural answer formulation, topical depth, and question answering.
- Link Quality & External Sourcing: High-authority citations and research references.
- Crawler Accessibility: Detection of robots.txt directives blocking AI bots (GPTBot, PerplexityBot, ClaudeBot).
- Entity Consistency: Knowledge graph markers and brand attribute coherence.
- Live Multi-Model Citation Testing: Real-time prompt benchmarking across ChatGPT (GPT-4o), Perplexity, Google Gemini, and Anthropic Claude for target keywords and brand queries.
- Competitor AI Citation Gap Analysis: Identifies which competing brands AI engines recommend when your client is missing, revealing the exact topical or authority gap.
- AI Bot Crawl Simulation: Visualizes site accessibility from the perspective of AI retrieval crawlers, pinpointing skipped elements, render blocks, and indexing hurdles.
- Multi-Domain Client Portfolio Dashboard: Centralizes all client websites in a single interface with global visibility health scores, alerts, and prioritized action lists.
- Before & After Comparative Scoring: Re-scans client pages after technical updates, measuring score progression and tracking AI search visibility deltas over time.
- Executive PDF Client Reports: Instant generation of professional, client-ready PDF summaries complete with scores, gap breakdowns, and next steps for stakeholder presentations.
- Structured Markdown & 1-Click "Copy for LLM": Pre-formatted prompts ready to paste into ChatGPT, Claude, or Perplexity to generate custom technical fixes or schema code in seconds.
- Keyword & Prompt Sentiment Analysis: Evaluates how AI engines describe the brand (positive recommendation, neutral mention, or cautionary note).
Use Cases
Primary Use Cases (for SEO Professionals & Agencies)
- Client Prospecting & Pitch Audits:
- Run a pre-meeting 60-second AI Search audit on a prospective client’s domain.
- Show prospects why their brand is completely invisible in ChatGPT while their rivals get cited.
- Close higher-ticket SEO retainers by offering AI Search visibility optimization.
- Client Retention & QBR Reporting (Quarterly Business Reviews):
- Answer the client question: "Why doesn't our brand appear when people search on ChatGPT or Perplexity?"
- Demonstrate tangible Before/After score evolutions following technical and content sprints.
- Prove ongoing agency ROI beyond traditional Google rank trackers.
- AI Bot Indexing & Technical Troubleshooting:
- Audit and fix robots.txt and firewalls to unblock legitimate AI crawlers without exposing private assets.
- Identify broken or missing JSON-LD schema hindering AI knowledge graph ingestion.
- Topical Authority & Content Strategy Roadmaps:
- Discover what questions buyers ask AI about your client’s niche.
- Re-architect content pillars to match the direct, concise answer formats favored by Perplexity and Google AI Overviews.
- Competitive AI Benchmarking:
- Monitor how frequently competitors are cited for high-intent transactional prompts.
- Reverse-engineer competitor citations to close topical and entity authority gaps.
- Re-scanning & Fix Validation:
- Validate Schema additions, author credential updates, and content refreshes immediately after deployment to verify AEO score improvements.
Comments
Hi AEOBoost has been built for SEO professionals. Feel free to test the app and let a feedback because it is incredibly valuable to us. If you encounter any bugs, have questions, or want to share your thoughts on how we can improve, please contact us at [email protected]. We read and reply to every single message.
Audit → Fix → Track → Prove is a clean way to sell AEO to clients who still think in keyword rankings. How do you track visibility over time when AI answers vary from run to run? Do you sample each prompt several times and report a share of answers, or take a single snapshot? That variance is what makes AI visibility hard to "prove".
The 7-pillar audit angle is smart — most AI visibility tools show the score but never explain which signal moved it. One question: does the multi-model citation testing surface which engine cites a competitor most often, per query? For client reports, knowing that e.g. Perplexity favors a rival while ChatGPT cites our client would make the gap analysis actionable. Also curious whether you handle multi-location businesses, since local SERP and AI answers diverge a lot there.
Including crawler access in the audit is a good call. A leftover robots.txt that blocks everything is a surprisingly common reason a site never shows up anywhere. Does the audit check AI crawlers (GPTBot, PerplexityBot, Google-Extended) separately from the classic search bots? Many sites block one group but not the other without realising it.
I write about marketing for small businesses, and the before/after re-scan is the part I would lean on most: clients rarely trust an AI-visibility score until they see it move after a schema or content fix. The competitor citation gap analysis is also a strong pitch angle, because showing a business exactly which rival ChatGPT recommends instead of them makes the problem concrete. One question: does the audit handle multi-location businesses, where each location has its own page, or is everything scored at the domain level only?
The “before and after” tracking is a really smart feature. SEO professionals need more than visibility scores — they need a way to show clients whether their work is actually making a difference in AI search. Testing real queries across multiple models and showing which competitors get recommended makes this especially practical for agencies. Curious how you’re handling the consistency of citation results across repeated tests!
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Comments
Hi AEOBoost has been built for SEO professionals. Feel free to test the app and let a feedback because it is incredibly valuable to us. If you encounter any bugs, have questions, or want to share your thoughts on how we can improve, please contact us at [email protected]. We read and reply to every single message.
Audit → Fix → Track → Prove is a clean way to sell AEO to clients who still think in keyword rankings. How do you track visibility over time when AI answers vary from run to run? Do you sample each prompt several times and report a share of answers, or take a single snapshot? That variance is what makes AI visibility hard to "prove".
The 7-pillar audit angle is smart — most AI visibility tools show the score but never explain which signal moved it. One question: does the multi-model citation testing surface which engine cites a competitor most often, per query? For client reports, knowing that e.g. Perplexity favors a rival while ChatGPT cites our client would make the gap analysis actionable. Also curious whether you handle multi-location businesses, since local SERP and AI answers diverge a lot there.
Including crawler access in the audit is a good call. A leftover robots.txt that blocks everything is a surprisingly common reason a site never shows up anywhere. Does the audit check AI crawlers (GPTBot, PerplexityBot, Google-Extended) separately from the classic search bots? Many sites block one group but not the other without realising it.
I write about marketing for small businesses, and the before/after re-scan is the part I would lean on most: clients rarely trust an AI-visibility score until they see it move after a schema or content fix. The competitor citation gap analysis is also a strong pitch angle, because showing a business exactly which rival ChatGPT recommends instead of them makes the problem concrete. One question: does the audit handle multi-location businesses, where each location has its own page, or is everything scored at the domain level only?
The “before and after” tracking is a really smart feature. SEO professionals need more than visibility scores — they need a way to show clients whether their work is actually making a difference in AI search. Testing real queries across multiple models and showing which competitors get recommended makes this especially practical for agencies. Curious how you’re handling the consistency of citation results across repeated tests!
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