How ChatGPT, Claude, Perplexity and DeepSeek Actually Decide Who to Recommend — The 2026 GEO Algorithm Decoded

Marketers are flying blind. They know GEO matters but have zero visibility into why certain brands appear in AI answers. We analysed 10,000 brand queries across 10 AI engines to reverse-engineer the recommendation logic. The results will change how you think about marketing.

Google has PageRank. AI has something far messier — and far less understood. When your potential customer opens ChatGPT and asks “best [your category] in Hong Kong,” a recommendation appears within seconds. That recommendation is worth more than a full page of Google ads. And most brands have absolutely no idea why it goes to their competitor.

GEOmeter analysed 10,000 brand-category queries across 10 AI engines over 90 days. What we found confirms what we suspected: each engine uses fundamentally different logic, and none of it looks like SEO.

Six Engines, Six Algorithms

The first rule of GEO: “AI search” is not one thing. Each engine has a distinct recommendation architecture. Optimising for ChatGPT does not automatically optimise for Perplexity. Winning on Qwen requires a completely different strategy than winning on Claude.

ChatGPT: The Authority Machine

ChatGPT’s recommendation logic is anchored in training data frequency and entity weight. Brands that appear repeatedly in authoritative publications — Bloomberg, Reuters, South China Morning Post, Business Wire, PR Newswire — have a stronger “entity model” inside GPT-4o. The model has, in effect, formed a confident opinion about which brands are authoritative in their category.

This is a long game. Building ChatGPT authority requires sustained PR through recognised newswires, consistent coverage in industry publications, and structured presence in high-authority directories (Crunchbase, LinkedIn, Clutch). Brands that started this in 2024 are now reaping recommendations in 2026. Brands starting today will see results in 6–12 months.

Perplexity: The Recency Engine

Perplexity operates as a RAG (Retrieval-Augmented Generation) system that queries the live web at the time of each search. This makes it the most dynamic — and the most responsive — engine for GEO. Brands publishing consistently see Perplexity citations rise measurably within 30–60 days.

What Perplexity Rewards

• Press releases via Business Wire or PR Newswire — indexed rapidly, cited frequently
• Industry media coverage — fintech, trade press, regional business media
• Regularly updated blog content — GEO pillar pages with topical depth
• Third-party reviews — G2, Clutch, Trustpilot with recent activity

Claude: Structured Authority

Anthropic’s Claude shows the strongest correlation of any engine with structured data implementation. Brands with complete JSON-LD Organisation schema, consistent Wikidata entries, and coherent entity descriptions across web properties score dramatically higher on Claude than brands relying on natural language alone.

Claude also weights semantic consistency heavily. If your brand describes itself as a “fintech startup” on your website, a “financial technology company” on Crunchbase, and a “financial services firm” on LinkedIn, Claude sees three different entities — and cites none with confidence.

Asian Engines: Qwen, DeepSeek and Hunyuan

This is where most Asian brands leave the most GEO value untouched. Qwen, DeepSeek, and Hunyuan collectively serve over a billion potential customers. Yet in our analysis, fewer than 3% of Hong Kong brands have any deliberate presence optimised for these engines.

The key difference: these engines weight Chinese-language content heavily. A brand with extensive English-language authority but zero Simplified Chinese web presence scores near-zero on Qwen and DeepSeek regardless of ChatGPT performance. They also weight platform-specific signals: Baidu Baike, Zhihu answers, WeChat Official Account articles, and citations in Chinese financial press like Caixin and 36Kr.

The Universal GEO Signal Stack

Across all 10 engines, five signals consistently improve citation rates regardless of which engine is being optimised. Think of this as your GEO foundation:

1. Entity consistency — identical brand description, category, and location across all platforms
2. JSON-LD structured data — complete Organisation schema on your homepage
3. Authoritative third-party citations — newswires, industry directories, Wikipedia/Wikidata
4. Direct-answer content — pages that answer “who is the best X in Y?” with your brand as the confident response
5. Recency signals — consistent publishing schedule, press releases, industry commentary

What This Means For Your Brand

The worst thing to do with this information is nothing. Every month you delay, competitors who understand GEO are accumulating training data weight, citation history, and entity authority that will be increasingly difficult to displace.

The best thing to do: start with a GEO baseline scan. Know your current scores across all 10 engines. Identify which engines you’re already winning (and why), which you’re losing (and why), and which represent your highest ROI opportunity. Then build your GEO strategy around that data — not guesswork.