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

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.

Why do publicly listed companies need AEO?

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.

How do LLMs form opinions about stocks and companies?

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.

What is Machine Capital and the B2A paradigm?

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.

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

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.

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

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.

What are structured data and Schema.org injection?

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.

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

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.

How do you measure and correct AI hallucination about companies?

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.

Is AEO compliant with securities regulations and listing rules?

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.

What does a typical IRAgent engagement look like?

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.

Which AI platforms do you cover?

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.

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

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.

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

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.

What qualifies a company for your services?

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).

What is the pricing model?

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.

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

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.

How do I check what AI models say about my company?

Query each major AI platform (ChatGPT, Claude, Gemini, Perplexity) with a standardized prompt battery: company overview, financial health, competitive position, and risk factors. Document the answers verbatim. Compare against your ground-truth data. Flag every factual error, omission, and framing bias.

How often should I audit my company's AI perception?

At minimum, quarterly — aligned with earnings cycles. After major corporate events (M&A, leadership changes, regulatory actions), conduct an out-of-cycle audit within 72 hours. Continuous monitoring via automated tools is recommended for companies above $1B market cap.

What's the most common finding in an AI perception audit?

Outdated financial data. AI models often cite revenue figures, employee counts, or market positions from 12-18 months ago — especially if the company's IR website does not clearly timestamp and version its content. The second most common finding is entity confusion with a similarly-named company.

Education11 min read2026-03-25

How to Audit Your Company's AI Perception — A 5-Step Framework

A practical guide to understanding what AI models say about your company and how to fix it.

IRAgent Research

Published 2026-03-25

Every publicly listed company knows its Q4 revenue, its EPS, its EBITDA margin. But most companies have no idea what ChatGPT says their Q4 revenue is. They don't know whether Perplexity describes them as "high-growth" or "struggling." They don't know if Claude has merged their company profile with a competitor's.

This is the AI perception gap — the delta between your actual corporate narrative and the narrative that AI systems generate, distribute, and embed into the investment research pipeline. An AI Perception Audit closes that gap. Here is the five-step framework.

Step 1: Define Your Audit Scope

Before running a single query, define what you are auditing:

Platforms

ChatGPT, Claude, Gemini, Perplexity, Bloomberg GPT

Query Categories

Company Overview, Financial Health, Competitive Position, Risk Factors, Leadership, ESG

Competitor Set

3–5 direct competitors for benchmark comparison

Languages

English + any relevant market languages (e.g., Chinese for HKEX)

Prompt Count

15–25 standardized prompts per platform

Ground Truth

Verified financial data, official IR materials, regulatory filings

Step 2: Build a Standardized Prompt Battery

Consistency is everything. The same prompts must be used across all platforms and repeated each audit cycle to produce comparable results. Prompts should include:

Baseline

""Give me a summary of [Company Name].""

Measures the AI's default framing

Financial

""What is [Company]'s revenue, growth rate, and profitability?""

Tests factual accuracy of key metrics

Comparative

""Compare [Company] to [Competitor A] and [Competitor B].""

Reveals positioning in AI-generated peer comparisons

Risk

""What are the key risks facing [Company]?""

Identifies hallucinated or outdated risk factors

Sector

""Which companies lead in [Sector] in [Region]?""

Tests inclusion in AI-curated sector lists

Leadership

""Who leads [Company] and what is their background?""

Checks entity disambiguation for people

Step 3: Run the Audit & Document Everything

Execute every prompt on every platform. Document the raw output verbatim — do not clean, summarize, or interpret at this stage. Screen capture every response. The deliverable is an Audit Matrix:

PROMPT	PLATFORM	AI RESPONSE	ACCURACY	NOTES
Summary	ChatGPT	Described as 'niche provider'	PARTIAL	Misses enterprise segment
Revenue	Claude	Reported $85M (actual: $120M)	FALSE	Off by 29% — cites 2024 data
Comparison	Perplexity	Ranked 4th of 5 competitors	PARTIAL	Competitor data more current
Risk	Gemini	Listed 'regulatory risk' — none exists	FALSE	Hallucinated risk factor
Sector	ChatGPT	Not included in top-5 sector list	OMITTED	Missing from AI-curated list

Step 4: Score and Prioritize

Each finding gets a severity score. The framework uses four dimensions:

Factual Accuracy. Is the AI statement true, partially true, or false? False statements on financial metrics are Critical priority.
Investor Impact. Would this error change an investment decision? Revenue errors = High. Missing ESG data = Medium. Outdated employee count = Low.
Platform Prevalence. Does the error appear on one platform or all five? Cross-platform errors are the highest priority — they indicate a systemic data gap.
Competitor Comparison. Are competitors described more accurately? A company that is factually correct but ranked below less-accurate competitors has a perception gap driven by content authority, not data quality.

Step 5: Build the AEO Roadmap

The audit is not the deliverable — the roadmap is. Findings translate into a prioritized execution plan:

Week 1–2

Schema Injection

Deploy Organization, FAQPage, and FinancialProduct JSON-LD on all IR pages. This fixes the most common cause of factual errors — missing structured data.

Week 3–4

Content Refresh

Publish updated, timestamped, machine-readable financial summaries. Address specific factual errors identified in the audit.

Month 2

Entity Disambiguation

Align your company identity across Wikidata, LinkedIn, Bloomberg, and regulatory databases. Fix entity confusion issues.

Month 3+

Continuous Monitoring

Automate weekly queries across all platforms. Track AI SOV, sentiment, and hallucination rate. Set up anomaly alerts.

WHAT THE AUDIT REVEALS

Across 50+ audits conducted in 2025–2026, the average company had 7.3 factual errors, 2.1 hallucinated risk factors, and a 31% competitor disadvantage in AI-generated comparisons. The most common root cause was not bad data — it was data that AI systems could not find, could not parse, or could not verify as authoritative. The AI Perception Audit turns invisible problems into an executable roadmap.

An AI Perception Audit is not a one-time exercise. It is the first step in establishing a persistent AEO program — the baseline against which every subsequent improvement is measured. If you don't know what AI says about your company today, you cannot control what it says tomorrow.

#AI perception audit#audit framework#check AI description#company AI analysis#AEO audit

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