The framework for understanding how AI systems discover, evaluate, recommend, and route customers to businesses.
For decades, growth started with traffic. More clicks, more impressions, more rankings. That worked when search engines were the main way people discovered products.
Today, millions of buying decisions start with a conversation. People ask ChatGPT, Gemini, Claude, and Perplexity which product, brand, or store they should choose. The challenge is no longer simply being found. The challenge is being recommended.
A business can be visible and still never be recommended. It can rank and still never be chosen. It can even be trusted and still lose to a competitor that is easier to understand. Before a business can be recommended, it has to move through several layers. It must become readable, then understood, then trusted. Only then does recommendation happen, and only then can a customer decide with confidence.
Each layer builds on the one before it: there is no understanding without readability, and no recommendation without trust. Go deeper into any single layer below, or read the full breakdown of every factor each one measures on the dedicated 7 Layers page.
Businesses will keep competing for attention, but attention alone will not be enough. They will increasingly compete to be understood, trusted, recommended, and chosen by intelligent systems. Atom Foundry researches how businesses become discovered, understood, trusted, recommended, and chosen, so they can prepare for that future before it arrives.
The overview above covers what the framework is and why it exists. The full detail, including every factor each layer measures and the data layer underneath it, lives on these two dedicated pages.
Find out exactly what AI agents see when they evaluate your store against all seven layers of the framework.
Illustrative example · single-site signal for atomfoundry.dev.
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