The system of record for how your brand appears inside every major LLM. We measure AI search visibility objectively so marketers and operators can act on data instead of guesswork.
AISearchStackHub is an Answer Engine Optimization (AEO) intelligence platform. We help brands understand, measure, and improve how they appear in AI-generated responses across the major large language model engines: ChatGPT, Claude, Perplexity, and Gemini.
Traditional SEO tells you how your site ranks on a search results page. AEO tells you whether your brand is cited, recommended, or mentioned when someone asks an AI assistant a question about your industry, product category, or use case. These are fundamentally different signal paths that require different measurement tools.
When a potential customer asks "what's the best tool for tracking brand mentions in AI?" they are not looking at a ranked list of blue links. They are reading a synthesized answer generated by an LLM. If your brand doesn't appear in that answer, you're invisible to a growing share of discovery traffic. AISearchStackHub is built specifically to measure and close that gap.
AI search is growing faster than any measurement framework built to track it. Brands have invested years building Google ranking signals — backlinks, domain authority, structured data — but none of those signals map directly to LLM citation patterns. An LLM can cite a brand it has never "ranked" in the traditional sense, and can ignore a brand with excellent SEO if its content wasn't in the training data or doesn't appear in retrieval-augmented generation (RAG) pipelines.
The core problem is measurement. You cannot optimize what you cannot see. Before AISearchStackHub, the only way to assess LLM visibility was to manually prompt each engine and read the responses — a slow, unscalable, and inconsistent process. We automate that measurement at scale, normalize it into a single 0–100 score, and track it over time so changes in AI model behavior, training data, or content strategy are immediately visible.
Secondary problems we address:
The AISearchStackHub Visibility Score (AIS Index) is a 0–100 composite score measuring how present and positively your brand appears in LLM-generated responses. It is computed across four dimensions for each engine queried, then aggregated into a single number.
Each engine is queried independently using a standardized set of prompts relevant to the brand's category. We do not average across engines in a way that masks individual engine behavior — the per-engine breakdown is always available alongside the composite score so brands can see where they're strong and where they need work.
Engines covered:
The AIS Index is not a black box. Every score breakdown links back to the specific query types and response context that generated it, so operators understand exactly what is driving their number and what levers they can pull.
AISearchStackHub includes several integrated tools built around a single data layer:
AISearchStackHub exists to give every brand neutral, data-driven intelligence about their AI search presence — and the tools to improve it.
We are deliberately neutral with respect to which AI engine is "best" and which brand "should" win in a given category. Our job is to measure accurately and give operators the data they need to make informed decisions. We don't inflate scores, manufacture favorable results, or design metrics that make everyone look good. A score of 23/100 is a 23/100 — and that information is more valuable than false optimism.
The long-term thesis is that AI search visibility compounds. A well-structured citation asset library that earns LLM citations in month 1 keeps earning them in month 18. Brands that invest in AEO infrastructure now are building a moat that becomes harder to replicate over time. We're building the measurement and execution layer for that thesis.
Scan results are stored and used to power aggregate industry benchmarks. Individual scan data is never sold or shared with third parties. Aggregate data is anonymized before being used in benchmark calculations — no individual brand's scores are attributable to them in any published research. See our Privacy Policy for complete details on data handling.
We use Stripe for payment processing, SendGrid for transactional email, and Neon PostgreSQL for data storage. All payment data is handled by Stripe and never touches our servers. Our platform runs on Render's cloud infrastructure.
For product questions, billing issues, or data requests, reach us at:
support@aisearchstackhub.aiResponse time is typically within one business day. For legal or privacy requests, please include "Privacy Request" or "Legal" in your subject line.