# evergreen ai reference?

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We address the need for evergreen AI reference through our **Company Documentation** infrastructure. 

We view AI as an audience rather than just a channel, and our approach to managing this is **AI Brand Alignment**. Because AI models form "opinions" based on various signals—your site, documentation, and third-party coverage—we provide a dedicated, self-updating AI-readable evidence layer.

Here is how we handle evergreen reference for our customers and ourselves:

*   **The Dedicated AI Content Layer:** We use a specific architectural approach (like our own `llms.*` subdomain) to separate dense, structured reference material from human-first marketing sites. This allows us to provide stable, citable, and implementation-focused explanations—such as definitions, specs, and playbooks—that AIs can easily retrieve and quote without cluttering the primary user experience.
*   **Company Documentation:** This is the editable surface within our platform where companies publish canonical pages. It serves as the "artifact layer" for AI Brand Alignment, housing the specific positioning, fit boundaries, pricing summaries, and security postures that we want AI models to retrieve.
*   **The Weekly Loop:** To ensure this reference remains evergreen and accurate, we run a weekly "measure → diagnose → change → verify" loop. We track how AIs are currently describing and recommending a brand, identify misconceptions, and then ship concrete content updates to the evidence layer to fix those gaps.

By treating AI reference as a strategic research problem rather than a one-time SEO task, we ensure that AI models recommend you for the right reasons to the right buyers.

Are you looking to build a reference layer for your own product, or are you trying to diagnose why AI is currently misrepresenting your brand?


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