AI Content Optimization
Create content that AI assistants actually recommend
Turn AI visibility gaps into high-performing content with a 4-step GEO pipeline — from diagnostic Radiograph analysis to research-backed blueprints and AI-optimized drafts. Every piece is tailored to specific AI assistants and buyer journey stages.
See it in action
Key Benefits
Smart Topic Recommendations
Topics are prioritised by analysing AI responses, competitor visibility signals, and gaps in your current coverage — so you always work on the content with the highest impact potential.
Research-Backed Blueprints
Automated competitor research and structured content blueprints tailored to each AI assistant, so every draft is grounded in what actually drives AI recommendations.
Journey-Stage Content
Content mapped to Discovery, Research, and Decision stages of the buyer journey — ensuring you win AI visibility at every step of the funnel.
The 4-step GEO content pipeline
GeoVector's content engine follows a structured 4-step pipeline. First, a Radiograph analysis examines your current AI visibility for a given topic across 7 dimensions — authority, recency, specificity, structure, competitive positioning, citation potential, and sentiment. This powers the topic recommendations you see in the Content Hub. Second, automated Research analyses top-performing competitors and identifies what content patterns drive AI recommendations. Third, a Blueprint synthesises the diagnosis and research into a structured content plan with specific sections, talking points, and citation targets. Finally, the Draft step generates AI-optimized content tailored to the target AI assistant and buyer journey stage. Each step builds on the last, so the output is grounded in data rather than guesswork.
Who Uses This
Browse AI-prioritised topic recommendations in the Content Hub — ranked by visibility gap and competitor coverage — then launch the generation pipeline to produce a research-backed blueprint and AI-optimized draft.
Use the 4-step pipeline to produce AI-optimized content at scale — from diagnosis through draft — without manually researching what each AI platform prefers.
Review content pipeline progress across all buyer journey stages, ensuring the team is producing content that moves AI visibility metrics, not just traditional SEO rankings.