We are building the end-to-end operating system for AI discovery — predicting what gets cited, generating what should be, and attributing every citation to the pipeline it produced.

When an AI system decides what to cite, it performs a geometric operation in embedding space, evaluating semantic relevance, structural format, and authority signals simultaneously. Our Citation Intelligence Engine learns this geometry. It scores content against any query before publication, identifies exactly where the gaps are, and surfaces what competitors are doing to get cited instead.

But knowing what gets cited is only half the problem. It's hard to connect a citation in LLMs to an actual conversion, lead, or dollar of pipeline. The entire attribution infrastructure of digital marketing was built for a world of clicks and referrers. That world is ending. We are building the measurement layer for what replaces it.

We are two brothers — one who built large-scale programmatic SEO and growth systems and took a company from zero to acquisition, the other who built ML retrieval and embedding architectures at IBM, Samsung, and a YC-backed startup. This problem sits at the exact intersection of everything we've spent our careers doing.