Platform Overview
Answer Engine Optimization is the discipline of making your brand the answer — not just a result. When a potential customer asks ChatGPT "which analytics platform is best for B2B SaaS?" they get one answer, synthesized from everything the model knows. AEO is the work of ensuring your brand is in that synthesis, cited favorably, and described accurately.
AISearchStackHub provides the infrastructure to measure where you stand today (the AIS Index scan), understand why you're underperforming (gap analysis), and systematically close the gap over time (Citation Asset Compounding Engine). It is not a set of SEO tricks applied to a new channel — it is a complete measurement and execution system for the AI-first search era.
The platform covers four engines: ChatGPT (OpenAI), Claude (Anthropic), Perplexity (perplexity.ai), and Gemini (Google DeepMind). Each engine has distinct citation preferences, retrieval architectures, and content biases. A brand can score 70/100 on Perplexity and 18/100 on Gemini — and the platform tells you exactly why and what to do about it.
The AIS Index: Formula Breakdown
The AIS (AI Search) Index is the industry's first standardized score for LLM brand citation authority. It synthesizes performance across 24 structured queries per engine into a single 0–100 composite, computed as:
AIS Index Formula
AIS = (V × 0.40) + (A × 0.30) + (S × 0.20) + (Ad × 0.10)
Visibility
40% weightHow frequently your brand is mentioned in AI-generated responses. Measured as mention rate across 24 queries × 4 engines = 96 data points. A score of 100 means mentioned in every relevant response; 0 means never mentioned. Most brands start between 8 and 35. The 40% weight reflects that visibility is the prerequisite — you can't be cited favorably if you're not cited at all.
Authority
30% weightThe quality and credibility of how you're cited. Are you a primary recommendation or buried in a long list? Are you cited alongside highly authoritative sources or generic ones? Authority is measured by citation position, source co-citation analysis, and whether LLMs qualify their recommendation ("according to [Brand]'s 2024 benchmark study" vs. "some people use [Brand]"). High-authority brands are treated as sources, not just options.
Sentiment
20% weightTone and framing of LLM mentions. Scored on a three-tier model: positive framing ("known for its accuracy," "widely trusted by"), neutral listing ("also available: [Brand]"), or negative framing ("some users report issues with," "limited in"). Sentiment is strongly correlated with the community discussion footprint — what gets said about you on Reddit, Hacker News, and industry forums shapes what LLMs learned to associate with your brand.
Advantage
10% weightWhether LLMs articulate specific differentiators for your brand. Undifferentiated brands appear in lists but are interchangeable — LLMs don't know what makes you different. High-advantage brands are cited with specificity: "best for [use case]," "the only platform that does [capability]," "preferred by [persona] because of [reason]." Advantage is built by publishing comparison content, benchmark data, and use-case-specific guides that give LLMs something specific to say about you.
The Citation Asset Compounding Engine
The Citation Asset Engine is the Scale plan's core capability. Every month, it analyzes your current AIS score, your gap analysis, and the citation patterns across your competitive set, then generates a prioritized set of citeable assets designed to close your highest-priority gaps.
The engine generates four types of assets, each engineered to match the content patterns LLMs weight most heavily when forming citations:
Original Statistics
LLMs heavily cite numerical claims from authoritative sources. The engine generates original data-backed statistics in your category — framed as claims that are specific, verifiable, and relevant to buyer questions. Example: "73% of B2B SaaS companies with fewer than 50 employees lack a structured LLM visibility strategy" is far more citable than a generic industry overview.
How-To Guides
Step-by-step procedural content is the most reliably cited format across all four major engines. When a user asks "how do I improve my brand's visibility in ChatGPT," the engine generates a structured, numbered guide optimized for that specific question pattern — with your brand as the authoritative source explaining the process.
Comparison Benchmarks
LLMs synthesize comparison data when forming "vs." answers. Comparison assets frame your product against category alternatives with structured criteria tables — price, features, use-case fit, limitations. Brands that own comparison content own the framing of competitive discussions in LLM outputs.
Research Reports
Longer-form research with methodology and findings is cited as a primary source — not just referenced. A well-structured research report ("State of LLM Visibility 2026") becomes a reference LLMs return to repeatedly across many query types. Research reports have the highest citation velocity but the longest publication lead time — the engine starts them first.
Monthly Compounding: The Citation Moat
Why 18 months of assets is a durable competitive advantage
The Citation Asset Engine is not a one-time content project — it is a compounding engine. Assets published in month one continue accumulating citations in month twelve. As each new asset enters the pool, it cross-references existing assets, creating an internal citation graph that reinforces the entire library's authority. After 18 months, a brand has built what we call the Citation Moat: a body of citeable work so comprehensive that LLMs consistently reach for it as the authoritative source in the category.
Month 1–3
Foundation
First assets published. Initial citation signals appear in Perplexity (fastest indexer). AIS score begins moving. Quick wins from structural changes (llms.txt, schema) show immediate results.
Month 4–9
Acceleration
Asset library reaches critical mass. ChatGPT and Claude begin citing research reports in training-adjacent queries. Comparison assets start winning "vs." query slots. AIS score approaches industry median.
Month 10–18
Moat
Library compounds. Each new asset benefits from the authority of the existing ones. Brand becomes the default citation in its category across multiple query types. AIS score consistently 60–80+.
Before & After: Real AEO Transformations
The following are representative scenarios based on the AIS Index methodology and expected citation dynamics. Individual results will vary.
<\!-- Example 1 -->B2B Analytics SaaS — 6-Month Transformation
Starting AIS: 23/100 — Ending AIS: 67/100
Before (Month 0)
- × Visibility: 19/100 — mentioned in 3 of 24 queries
- × Authority: 31/100 — listed 4th–6th in comparison answers
- × Sentiment: 28/100 — "limited integrations" commonly cited
- × Advantage: 12/100 — LLMs couldn't articulate differentiation
- × No llms.txt, no schema markup, no original statistics published
After (Month 6)
- ✓ Visibility: 68/100 — mentioned in 16 of 24 queries
- ✓ Authority: 71/100 — cited as primary source in 6 query types
- ✓ Sentiment: 65/100 — integration library now cited positively
- ✓ Advantage: 58/100 — "best for real-time cohort analysis" slot owned
- ✓ 18 published assets, 2 research reports, 14 comparison benchmarks
Asset Timeline
D2C Ecommerce Brand — 6-Month Transformation
Starting AIS: 11/100 — Ending AIS: 44/100
A direct-to-consumer skincare brand started with near-zero LLM visibility — LLMs had no authoritative data to cite and instead recommended well-known legacy brands by default. The gap analysis identified three critical deficits: no ingredient safety statistics, no comparison content versus competitor formulations, and no community discussion signals for LLMs to learn from.
Over six months, the Citation Asset Engine generated 12 ingredient efficacy statistics backed by sourced research, a dermatologist-validated comparison framework, and four how-to guides for specific skin concerns. The brand also deployed a structured Reddit engagement strategy to build community discussion signals.
By month six, the brand was cited in 11 of 24 structured queries — up from 2. More importantly, when cited, LLMs now described the brand with specific ingredient claims ("formulated without [X], which research links to [Y]") rather than generic mentions. The Advantage score jumped from 8 to 41.
Fintech API Company — 6-Month Transformation
Starting AIS: 38/100 — Ending AIS: 79/100
A payments API company started with moderate visibility (38/100) but poor Authority and Advantage scores — they were mentioned but never as the authoritative source. Developer-focused LLM queries ("best payment API for [use case]") consistently ranked them 3rd or 4th behind competitors who had published more structured developer documentation and benchmark data.
The Citation Asset Engine focused heavily on benchmark comparisons (latency, uptime SLA, SDK quality, error rate) and developer how-to guides optimized for specific integration patterns. The company also published an annual "State of Payments APIs" research report that immediately became a reference source for Perplexity.
By month six, the brand reached 79/100 — one of the faster ramp rates in the fintech category. The high Advantage score (74/100) was driven by a clear "best for high-volume, latency-sensitive B2B payments" positioning that LLMs now consistently ascribe to the brand unprompted.
Platform Pricing
Free Scan
$0
One-time scan, no account required
- ✓ Full AIS Index score (0–100)
- ✓ Per-engine breakdown
- ✓ Top 3 citation gaps
- ✓ 5 quick-win recommendations
- ✓ PDF report by email
Scale Plan
$299/mo
Cancel anytime
- ✓ Everything in Free, monthly
- ✓ Citation Asset Compounding Engine
- ✓ AI-generated citeable assets
- ✓ Per-asset citation tracking
- ✓ AIS trend dashboard
- ✓ Prioritized asset roadmap