观察 XEO 是否提升真实世界的搜索发现
XEO 实验室将我们的基础设施应用于活跃的网站、书籍和研究组合。我们公开原始数据、干预措施、测量结果和局限性，以便访问者了解哪些工作有效，哪些仍在测试中。
当前证据
Scholarly Discovery Experiment
Real baseline across 14 SSRN publications:
- 1,050 cumulative abstract views
- 305 cumulative downloads
- 29.0% downloads-to-views ratio
What this establishes: A verified public baseline now exists.
What happens next: Comparable post-deployment snapshots will measure whether discovery and engagement change.
Book Discovery Experiment
Attributable Amazon sales and KDP royalties tracking:
- Outbound clicks: Awaiting certification
- Attributed sales: Awaiting Attribution data
What this establishes: Attribution architecture configured.
What happens next: Pre-intervention baseline logs will initialize once tracking setup is certified.
Latent Seeding Protocol
Map entity links inside LLM latent vector indexing systems:
- Evidence state: Protocol review
What this establishes: Measurement methodologies drafting.
What happens next: Release of reproducible seeding steps and mapping baselines.
XEO 如何进行测试
Collect verifiable pre-intervention metrics directly from public platforms or dashboards to establish starting conditions.
Inject machine-readable discovery assets, sitemaps, citation metadata, and structured topics.
Extract comparable post-deployment snapshots to determine if visibility or engagement shifts.
Evidence Standards Metrics Hierarchy
XEO structures its discovery testing protocols around a strict metrics hierarchy to separate genuine business outcomes from exploratory signals. This standards framework applies uniformly across organic search, AI referrers, citations, conversions, book sales pathways, and research portfolio visibility.
Tier 1 — Business Outcomes (Primary KPI)
Leads, purchases, subscriptions, qualified applications, and revenue changes.
Tier 2 — First-Party Acquisition
LLM referral traffic visits, organic search clicks, landing-page engagement, and assisted conversions.
Tier 3 — Retrieval Observations
URL-level citations, source frequency, citation position, and prompt coverage.
Tier 4 — Technical Diagnostics
AI crawler visits, schema validity, metadata structure, canonical setups, sitemaps, and llms.txt compliance.
Tier 5 — Weak / Exploratory Signals
Unlinked brand mentions, generic visibility scores, aggregate "AI share", and isolated search screenshots.
免费工具
AI Search Auditor
Benefit: Instantly scan how search bots and LLMs ingest and perceive your primary domain authority.
llms.txt Linter
Benefit: Validate structural syntax, compliance weights, and routing paths for your machine-readable sitemaps.
Concept Gap Scanner
Benefit: Audit your primary semantic keyword associations against competitor datasets inside active AI model indexes.
Apply the same discovery infrastructure to your work
Establish visibility benchmarks, deploy compliant sitemaps, and monitor discovery changes.