Lead AI-source identification
Source fields, referrer and domain patterns identify which AI engine a lead came from, stored on the lead record.
Platform Attribution
AI mentions you. Does it turn into business?
Lead forms, AI crawler visits, answer sources and article citations — four layers connecting "exposure in AI" with real business conversion.
What it solves
- Monitoring reports answer "were we mentioned"; leadership asks "did we win any business".
- Traffic and leads from AI channels never reach the ledger.
- Attribution is where self-deception is easiest — mistaking correlation for causation is the most expensive mistake.
How it works
- Lead forms with SLA follow-up: configurable forms and a lead progression flow.
- AI crawler analytics: which AI crawlers visited and what they fetched.
- Channel-level citation attribution: answer source domains vs owned channels.
- Article-level citation attribution: whether each published article gets cited (reported as a lower bound; unmatched counted as "unmatchable", never dressed up as a conclusion).
Capabilities
Four attribution layers, each verifiable
Channel-level citation attribution
Answer source domains matched against owned channels — suited to owned-site scenarios.
Article-level citation attribution
Each published link compared against AI source URLs to answer "was this article cited?"; unmatched sources are honestly counted as unmatchable.
GEO ROI calculator
Sampling and content costs alongside lead volume — a practical read on return.
Lead forms & SLA
Configurable forms catch AI-channel visitors; lead progression has states and timeout reminders.
Effect-boundary statements
Every attribution report states what it can and cannot imply — correlation is never dressed up as causation.