Articles
Most AEO advice tells banks and fintechs to rewrite their web pages so AI models will cite them. I think that misses what's actually happening. Agents that know their users in detail don't browse for a page to quote; they query for products that fit, and they act on the answer. That makes product discovery a data problem, and it makes agents bottom-of-funnel lead sources that should be paid on conversion. These articles make that case, one piece at a time.
Product discovery is moving from web pages to APIs and MCP servers
Why I think product discovery is shifting from HTML built for people to data that agents query, and what Bancadia is building for it.
In AI search, the agent is the lead: why affiliate networks will overtake ad networks
Agents arrive at a recommendation already holding the user's full context. That makes them bottom-of-funnel lead sources, and conversion-based pay follows.
AI agents know everything about their user. Your product data has to keep up.
An agent's context only helps if it can be turned into filters. If your product attributes aren't structured, the agent can't match you to the user you fit best.
What AI agents need from product data: fresh, structured, token-efficient and accurate
The four requirements for product data that agents can recommend from, with a real token measurement of page scraping versus an MCP query.
Affiliate content was written for a generic reader. AI agents know exactly who they're helping.
Listicles had to be broad because their author knew nothing about the reader. Agents know everything, and they need a data layer, not an article.