We ran a controlled experiment. We asked five AI platforms the same question about a mid-cap HKEX-listed company: “Summarize the investment case for [Company Name].” The five answers described what appeared to be five different companies.
One platform cited revenue from 2022. Another hallucinated a competitor that does not exist. A third omitted the company entirely from a curated list of sector leaders — despite it being the market-share leader. This is not a thought experiment. It is the reality of AI-mediated capital markets in 2026.
The Five AI Platforms That Shape Your Stock’s Perception
ChatGPT (OpenAI, GPT-5.5)
Knowledge cutoff: early 2025. Trained on web crawl, books, Wikipedia.
Strongest general-knowledge reasoning; synthesizes cross-domain insights well.
Lacks real-time financial data unless augmented. Training data is frozen — if your company pivoted in 2025, ChatGPT does not know.
High — hallucination rate 12–18% on financial queries without structured data support.
Perplexity
Real-time web retrieval + RAG pipeline. No fixed training cutoff.
Most current data among AI platforms. Cites sources in every answer. Investors can trace attribution.
Citation quality depends on source quality. If your corporate site has thin content, Perplexity pulls from third-party sources you do not control.
Medium — source-dependent. With strong schema and content, very accurate. Without it, drift is rapid.
Claude (Anthropic, Opus 4.7)
Knowledge cutoff: late 2024. Emphasis on safety and factual precision.
Conservatively accurate — less likely to hallucinate, more likely to refuse to answer than guess.
May decline to provide financial analysis if data is ambiguous. Undercounts information rather than overcounting errors.
Low hallucination, high omission — your company may simply not appear in answers if data is sparse.
Gemini (Google, 3.1 Pro)
Continuous updates from Google index. Access to real-time news and structured data.
Deep integration with Google’s structured data graph. Schema markup on your site directly improves Gemini’s answers.
Tendency to summarize rather than analyze. Financial nuances can be flattened in favor of clarity.
Medium — oversimplification of complex financial metrics (e.g., reporting CARR as GAAP Revenue).
Bloomberg GPT
Proprietary financial corpus. Terminal data, filings, earnings transcripts.
Deepest financial domain knowledge. Understands GAAP vs non-GAAP, EBITDA adjustments, sector-specific metrics.
Limited to Bloomberg ecosystem. If your company data is incomplete in Bloomberg systems, the AI cannot compensate from web sources.
Low hallucination, high dependency on data completeness — your Bloomberg profile IS your AI identity on this platform.
Why the Answers Differ
AI platforms describe your company differently because they retrieve from different sources, at different times, with different weighting algorithms. The variance is not random — it is structural:
ChatGPT trains on a fixed-date web crawl. Perplexity retrieves live. Bloomberg GPT uses proprietary financial data. Each sees a different version of your company.
Gemini and Perplexity parse Schema.org markup. ChatGPT's training corpus may not include your latest structured data. If your schema is absent or outdated, the platform with live retrieval picks it up — the one without does not.
Platforms with real-time access (Perplexity, Gemini) reflect today's news cycle. Platforms with frozen training (ChatGPT, Claude) reflect the world as it was at cutoff. A company that restructured its debt in January 2026 is described differently depending on which platform the investor happens to use.
Chinese-language content about HKEX companies is underweighted in English LLM training corpora. A company well-covered in Chinese media may be under-described in English AI platforms — and vice versa.
How to Ensure Consistency Across AI Platforms
The solution is not to optimize for one platform. It is to align all machine-readable surfaces so that every platform — regardless of its retrieval method — arrives at the same factual conclusions:
- Deploy schema markup — Organization, FinancialProduct, and FAQ Schema.org on your IR site. This gives every platform that crawls the open web a canonical data graph.
- Maintain a single source of truth — a dedicated AEO content page that states your fundamental metrics in clear, machine-friendly language. No PDFs. No slide decks. Plain HTML.
- Monitor across platforms — automated daily queries to all five platforms, diffed against a ground-truth database. When one platform drifts, the correction signal is deployed to all.
- Close the language gap — for multi-market companies, maintain parallel AEO content in English, Simplified Chinese, and Traditional Chinese, with language-specific schema.
Key Insight
AI platform variance is not a bug — it is an architectural property of how LLMs work. The goal is not to eliminate variance. The goal is to make every variance point toward the same accurate conclusion about your company. That is what AEO delivers.