Content authority in AI encompasses the signals that help AI platforms recognize your pages as credible sources worth citing or referencing. It extends E‑E‑A‑T into AI contexts where extractability, provenance, and third‑party validation are equally important.
Key authority signals for AI
Provenance matters. We clearly attribute authorship, include credentials, and maintain an about page that explains who we are and how we conduct research. Evidence matters. We support claims with original data, benchmarks, screenshots, and customer outcomes, then include a reference list. Coverage matters. We contribute to reputable publications and documentation hubs so that our message appears in places models already trust. Consistency matters. We align naming and facts across our site, docs, partner listings, and review platforms. Structure matters. We use extractable formatting so models can quote us accurately.
How to build authority
We publish research with a methods section that explains sampling and timing. We earn expert quotes and occasional guest features in trusted outlets by offering specific insights rather than generic opinions. We standardize naming and entity descriptions so our brand and product lines are described the same way everywhere. We keep cornerstone pages updated with visible dates and change notes so retrieval based systems can verify recency.
LLM Pulse connects authority signals to outcomes by correlating pages and domains with citation frequency, mentions, and sentiment trends across platforms.
Authority checklist
- Authorship: Expert bios, credentials, and ways to verify expertise.
- Citations: Link to reputable sources; include a references section for research posts.
- Evidence: Screenshots, data tables, benchmarks, or case studies.
- Transparency: Dates, update notes, and methodology disclosures.
- Consistency: Align claims across docs, marketing pages, and third‑party listings.
Building authority off‑site
We contribute to industry publications with evidence backed content that uses clear headings, short sections, and compact tables. We provide expert quotes through journalist networks, which often land in articles that are reused by AI systems. We encourage detailed reviews on G2 and Capterra that explain use cases and outcomes, and we reference those reviews in our own explainers where appropriate.
Template and governance
- Templates: Standardize page sections (TL;DR, methods, evidence, tables, FAQs).
- Content reviews: Require SME review and reference checks for cornerstone pages.
- Update cadence: Refresh high‑impact pages quarterly; log changes with dates.
Pitfalls
- Claims without evidence; vague superlatives; outdated pricing/features.
- Inconsistent naming across properties; conflicting product descriptions.
- Walls of prose without extractable structures.