Diagnose, prescribe, produce, prove.
Four steps. One workflow. AVO AI walks the full chain from measurement to verified outcome — and feeds every result back into the network so the next domain learns faster.
Each link grounded in real measurement.
Most platforms automate one part of the chain and leave you to fill the rest. AVO AI runs the full sequence — and refuses to skip the verification step at the end, because that's the step that makes everything else better next time.
Diagnose — where is the gap?
AVO AI starts with two scores. Authority Score audits your domain's structural readiness for AI citation across three pillars. Visibility Score probes six AI platforms with unlimited prompts per topic to measure whether AI actually mentions, recommends, or cites you. The gap between the two tells AVO AI whether your problem is reality or readiness.
Prescribe — what to do, in what order.
Generic GEO checklists ignore sequencing. AVO AI doesn't. It reads the diagnosis, watches what your competitors recently changed, and prescribes a dependency-aware action plan. Fix crawlability before writing content. Strengthen entity signals before pursuing citations. The order matters — and the order is grounded in what worked across the network.
Produce — write the brief, draft the article.
Most tools tell you what to write. AVO AI writes it. The Content Engine takes the prescription, generates a structured brief, and produces a publication-ready draft in your brand voice and target language — including Chinese, Japanese, Korean, RTL, and partner canonical languages. No second tool. No copy-paste. No LLM wrapper.
Prove — measure the outcome, feed the network.
After you ship, AVO re-probes. New Authority Score. New Visibility Score. The delta becomes part of the verified outcome record — anonymised at the aggregate level, fed back into AVO's network intelligence. So the next domain that asks for similar advice gets a sharper projection, with your move as part of the evidence.
Why the network matters.
AVO's intelligence isn't trained on a static dataset. It grows with every measurement, every fix applied, every outcome verified. Here's why that compounds — and why it's hard to copy.