Using Recro simulation data, we look at why brands appear in AI answers when asked by name but disappear in category questions, through the eyes of the buyer who doesn't know them yet.

We look at what AI answers when procurement and finance leaders ask about price, the pricing-signal gap in Recro's simulations and what publishing a pricing model changes.

Using findings from Recro's own simulations, we look at which off-site sources AI relies on when recommending a B2B brand, from employer profiles to industry cluster pages.

We look at why well-known brands aren't recommended in AI answers, how the Authority GAP forms and how to turn field authority into digital proof.

We look at how AI systems read a web page, which gaps human-first pages leave for machines and how to test for them.

We look at how B2B buyers build the shortlist before talking to vendors, the four gates where brands are silently eliminated and the signs of that elimination.

We look at why sales inquiries don't rise as web traffic falls, through zero-click search, the shortlist formed in AI answers and measurement blind spots.

We look at why AI describes a brand as outdated, incomplete or wrong, the six sources of misrepresentation and the order in which to correct it.

We look at how to read a competitor's signal from the rationale AI gives when recommending them, and how to build your own equivalent signal.

We look at why brands that are strong on Google don't appear in ChatGPT answers, through differences in questions, indexes, source types and model memory.