Brands
Two kinds of brand run on the same engine: the partner brands we build and power, which is where most of it runs today, and decision resources of our own, which we are building now.
Partner brands
The engine currently powers a five-million-page higher-education decision resource and a ten-site education network for a partner: college, program, career, and ranking pages generated from a knowledge graph of more than a hundred data sources, refreshed as the data changes, with structured data, honest dates, and a curated index. That partner is also the proving ground for our AI search program, with the first measured readout planned for early 2027.
We do not name partner brands or their sites without permission. Ask us in conversation.
Our own brands
We are building decision resources of our own on the same engine. Each stacks the full revenue architecture: audience revenue, AI contribution and licensing, products for the entities it ranks, and demand it owns.
None is live yet. We will name them when they launch.
What makes a vertical a fit
Schools, providers, products, places, employers, ideally related to each other, so the knowledge graph has structure to exploit.
Many factors, a large price tag, and stakeholders who lack a neutral source.
Public, licensed, or gathered data describing the options and their outcomes, refreshed on a schedule so freshness is built in.
A query space where assistants answer most questions, so being the cited source is the game rather than an option.
Leads, affiliate, advertising, entity products, licensing, or contribution programs, ideally several.
No incentive to fabricate, no regulated advice given by a machine, no need for personas that do not exist.
Tell us the decision your audience faces and the data that describes it. If it passes the six tests, the first conversation is about what the brand would look like.