Baiyuan GEO Platform Technical White Paper: SaaS Engineering Practice for Generative Engine Optimization (GEO)

Based on the GEO Platform technical white paper of Baiyuan Technology, this article analyzes its SaaS engineering practice for generative AI visibility. The core includes the seven-dimensional AI citation rate scoring algorithm, AXP shadow document delivery mechanism, Schema.org three-layer entity knowledge graph, and the hallucination automatic detection and repair closed-loop system, helping brands get accurate and continuous mentions in generative AI such as ChatGPT and Claude.

Generative AI is developing rapidly from 2024 to 2026, with ChatGPT's monthly active users exceeding 400 million and Perplexity's daily queries reaching tens of millions. Users expect AI to provide structured answers directly, spawning Generative Engine Optimization (GEO) — unlike traditional SEO which focuses on web page rankings, GEO aims for brands to be correctly mentioned in AI answers. Baiyuan Technology has invested two years to develop the GEO Platform to respond to this trend.

GEO is a paradigm shift: optimizing the existence quality of brand entities in AI knowledge graphs. The core evaluation dimensions include citation accuracy, location quality, query coverage, platform breadth, sentiment tendency, content depth, and consistency, which form the basis of the seven-dimensional scoring algorithm.

AXP (AI eXchange Protocol) is a document delivery mechanism designed specifically for AI Bots, using a hybrid format of pure HTML + Schema.org JSON-LD + Markdown. Its features include structured data priority, a clean content layer, Cloudflare Workers edge deployment, and multi-tenant architecture, providing machine-first content without conflicting with existing websites.

A three-layer entity knowledge graph is built based on Schema.org: the first layer is organizational entities (basic information such as name and official website); the second layer is service and product entities (functions, pricing, etc.); the third layer is personnel entities (founders, etc.). The three layers are interconnected via @id, helping AI establish brand semantic associations and improve citation probability and accuracy.

To address the AI hallucination problem, the platform has established a closed-loop system: sentinel scans every 4 hours and full scans every 24 hours to detect errors; mark errors with ClaimReview; update the knowledge base via AXP/RAG; continuously track repair effects to form a data closed loop.

Stale Carry-Forward Strategy: Proactively inject the latest information, anchor key facts with timestamps and sources, and use multi-source verification to solve AI knowledge lag; Multi-Provider Routing: Differentiated optimization for ChatGPT (dialogue context), Perplexity (citation authority), Claude (long text), Gemini (Google ecosystem); The tech stack includes Cloudflare Workers front-end, PostgreSQL database, RESTful API, CI/CD deployment, and full-link monitoring.

Current limitations: AI black box makes it impossible to fully control generation, platform policy risks, cross-language challenges. Future outlook: Expand to multi-modal (images, videos, etc.), v2.0 will integrate visual entity recognition capabilities. Conclusion: GEO is key to brand communication in the AI era, and being correctly mentioned is an important asset.