Question 1

What is AEO (Answer Engine Optimization) and how is it different from SEO?

Accepted Answer

Answer Engine Optimization (AEO) optimizes digital content so AI systems — not search engines — correctly retrieve, cite, and describe your company. SEO targets ranked links for human users; AEO targets the factual layer inside Large Language Models. When an investor asks ChatGPT about a company, SEO determines page rank; AEO determines whether the AI mentions you at all, and with what framing.

Question 2

Why do publicly listed companies need AEO?

Accepted Answer

Because the due diligence workflow has already changed. Bloomberg terminals embed GPT, analysts use Claude for instant summaries, fund-of-funds screen with Perplexity, and retail investors ask Gemini about stocks. AEO ensures your corporate narrative travels intact through the AI pipeline that now mediates the majority of investment research.

Question 3

How do LLMs form opinions about stocks and companies?

Accepted Answer

LLMs retrieve from a probabilistic corpus built from structured databases, web content, financial filings, news, and social sentiment. An answer is shaped by three factors: structured data signals, surface-area and authority of web content, and consistency checks across multiple sources. AEO aligns all machine-readable surfaces to ensure accurate representation.

Question 4

What is Machine Capital and the B2A paradigm?

Accepted Answer

Machine Capital is capital allocation executed, filtered, or guided by algorithmic and AI-driven systems. B2A (Business-to-Agent) recognizes that a growing share of investment decisions flows through AI agents. IRAgent is the world's first B2A platform, bridging traditional IR disclosure with the structured, machine-readable layer that AI capital demands.

Question 5

How is AI-driven due diligence different from traditional analyst research?

Accepted Answer

AI due diligence is parallel, probabilistic, and persistent. It cross-references dozens of sources simultaneously and never forgets past negative news cycles. You cannot spin an AI — you can only ensure the signal-to-noise ratio favors accurate information through structured data and authoritative content seeding.

Question 6

How does IRAgent influence what AI models say about a company?

Accepted Answer

Through four white-hat channels: (1) Schema and Structured Data Injection — embedding JSON-LD into web properties; (2) High-Authority Content Seeding — publishing entity-optimized content; (3) Vector Database Alignment — ensuring content embeddings are present in RAG pipelines; (4) 24/7 Monitoring and Correction — detecting anomalies and triggering re-injection. No model weights are manipulated.

Question 7

What are structured data and Schema.org injection?

Accepted Answer

Structured data is a standardized vocabulary (Schema.org) that describes entities in a format machines can parse without ambiguity. JSON-LD markup declaring company name, ticker, industry, revenue, and leadership allows AI crawlers to ingest ground truth rather than guessing from unstructured text — preventing hallucination.

Question 8

What is AI Share of Voice (AI SOV) and how do you measure it?

Accepted Answer

AI Share of Voice measures a company's presence, sentiment, and accuracy across AI-generated answers relative to its peer set. We track four dimensions: Mention Rate, Sentiment Polarity, Factual Accuracy, and Hallucination Rate — benchmarked against a self-selected competitor set in a quarterly AI Perception Report.

Question 9

How do you measure and correct AI hallucination about companies?

Accepted Answer

We query each target AI platform daily with a standardized prompt battery, parse responses, diff against ground-truth data, and score for hallucination. When detected, correction triggers multi-surface re-injection: updated structured data, refreshed authoritative content, and targeted entity disambiguation signals.

Question 10

Is AEO compliant with securities regulations and listing rules?

Accepted Answer

Yes. All work operates within standard IR disclosure principles. We do not create false statements or fabricate data. Our role is to ensure accurate, verifiable, publicly disclosed information is correctly structured and surfaced for AI systems. We recommend client legal counsel review all content prior to deployment.

Question 11

What does a typical IRAgent engagement look like?

Accepted Answer

Four phases: (1) AI Perception Audit (5 business days, complimentary) — mapping how every major LLM describes your company; (2) Schema and Vector Injection (2–4 weeks) — structuring corporate data; (3) Narrative Translation (ongoing) — converting IR materials into AI-optimized formats; (4) Continuous Monitoring (retainer) — 24/7 AI SOV tracking with monthly reporting. Full programs typically 3–6 months.

Question 12

Which AI platforms do you cover?

Accepted Answer

Standard coverage includes OpenAI GPT-5.5, Claude Opus 4.7, Google Gemini 3.1 Pro, Perplexity, and Bloomberg GPT. Platform coverage expands based on client industry and target investor geography. Enterprise clients receive coverage for any new major LLM release within 72 hours.

Question 13

How is this different from hiring a traditional PR or IR agency?

Accepted Answer

PR and IR agencies write for human audiences. IRAgent output is architected for machines — deliverables include structured data, embeddings, and FAQ schemas. Measurement uses AI SOV and hallucination rate instead of media impressions. Many clients retain both: an IR agency for traditional media and IRAgent for the machine layer.

Question 14

What happens when AI models update and change their descriptions of us?

Accepted Answer

Model updates are the single largest source of volatility in AI-generated corporate descriptions. Our 24/7 monitoring detects anomalies within hours and triggers re-injection protocols to restore accurate signals. AEO is not a one-time project — it is a persistent operational requirement.

Question 15

What qualifies a company for your services?

Accepted Answer

We work with publicly listed companies, pre-IPO firms in active fundraising, and companies with market capitalizations above approximately $500M USD. Exceptions are made for high-growth companies in sectors with heavy AI analyst coverage (SaaS, fintech, biotech, clean energy).

Question 16

What is the pricing model?

Accepted Answer

Custom-quoted based on: number of AI platforms covered, complexity of the entity graph, and cadence of monitoring and reporting. Initial AI Perception Audit is complimentary (5 business days). Full engagement pricing is discussed after the audit when both parties have data-driven understanding of the AI perception gap.

Question 17

Do you support multi-market and multi-language IR (HKEX + English + Chinese)?

Accepted Answer

Yes. Multi-market and multi-language coverage is core to the platform. We optimize across English, Simplified Chinese, and Traditional Chinese — covering major AI platforms in each language ecosystem. For HKEX-listed companies, this ensures correct representation in both English and Chinese LLMs. Native Chinese-speaking AEO specialists maintain separate quality benchmarks for each language track.

股票市場由智能體主導。

The Hallucination Penalty

IPO Prospectus

01 Audit

02 Inject

03 Bridge

04 Monitor

The Engine

AI Share of Voice

Targeted Protocols

Pre-IPO Valuation Optimization

Algorithmic Crisis Inoculation

M&A Narrative Alignment

The Control Plane

Real-time Hallucination Detection

Semantic Injection API

Competitor SOV Tracking

A HKEX Main Board Listed Company — AI Perception Turnaround

Before AEO

After AEO

常見問題

B2A 時代的投資者關係

AEO 如何運作

合作與交付

Get in Touch