Pipeline walkthrough
v0 is wired as a fixed, operator-run pipeline: a single domain enters, and a schema-governed YAML meta comes out the other end. Nothing about how any engine works is exposed — to the outside world the composition is a single black box.
Input: a domain
The operator supplies nothing but the prospect's domain — fitting the black-box, pre-sales model where the prospect has not engaged.
Audit probes the engines
Query set and competitors are derived automatically; each query runs repeatedly across the active providers, sampling the non-deterministic distribution.
Diagnosis finds root causes
The measured gaps are mapped to owned-content levers and ranked into a prioritized account of what holds the prospect back.
Generation ships the meta
The fix is emitted as a schema-governed YAML meta — titles, schema markup, llms.txt — the proof of work carried into the prospecting conversation.
Illustrative audit output
Below is the shape of an audit report for a fictional prospect. It is not a real result — v0 measures and diagnoses, but never applies the meta or re-measures, so it cannot yet show lift. The headline metric is AI Share of Voice: the prospect’s mentions and citations as a percentage of all category brand mentions.
acme.com. v0 measures and diagnoses
but never applies the meta or re-measures, so it cannot yet show lift.