Know Where to Publish and What to Do

Source analysis: where should you be present? ($3)

Every AI engine pulls information from specific sources — review platforms, content sites, industry directories, marketplaces. The system analyzes all resources cited across your monitoring results and categorizes them into three groups:

• Content platforms — blogs, media outlets, wikis where AI pulls expert content. Each platform shows priority level, what type of content to publish, and which AI engines cite it most
• Review platforms — sites like G2, Trustpilot, Capterra where AI pulls ratings and user opinions. Specific actions for each: claim your profile, respond to reviews, generate testimonials
• Marketplace platforms — Amazon, industry-specific marketplaces where AI pulls product data and pricing

Source analysis costs $3 per run and uses your latest monitoring data to identify the platforms that matter for your specific industry and location.

Missing platforms and blind spots

The most valuable insight is what’s absent. Missing platforms are sources where your competitors are present but you are not. These are highlighted with specific actions — because this is exactly where you’re losing AI visibility to competitors who took the time to claim their profile or publish a guest post.

Blind spots show specific queries where your brand has zero mentions across all engines. These are the customer questions where AI doesn’t know you exist — and each one is a concrete opportunity. A blind spot on “best [product] for enterprise” means you need content that positions you in the enterprise segment.

The Competitor Content Map shows which platforms your top competitors use and how much coverage they get on each. You see exactly what your competitors did to earn their AI visibility.

AI-generated promotion strategy ($2)

Based on your monitoring data, competitor landscape, source analysis, and real-time market intelligence, the system generates a phased promotion strategy.

The strategy is organized as a 3-phase roadmap:

• Quick Wins — actions you can execute this week with immediate impact on AI visibility
• Medium-Term (1–3 months) — content campaigns, platform registrations, and relationship-building that compound over time
• Long-Term (3–6 months) — authority-building, entity optimization, and structural changes to your digital presence

Each phase opens with an executive summary explaining the rationale, expected aggregate impact, and key dependencies. Inside each phase, every recommendation follows the format:

• Fact — the data point from your monitoring that triggered this recommendation
• Action — a concrete task with clear steps
• Expected result — which metric should change and by how much (“Market SOV +12–18%”)
• Verification timeline — when to recheck (“recheck in 21 days”)

The strategy also includes an engine-specific playbook — tailored optimization tactics for each AI engine based on how it ranks and recommends brands differently.

Strategy generation costs $2 per run. Strategies are generated per location — a strategy for Milan will differ from London because each market has its own competitive landscape and platform ecosystem.

Hypothesis testing: prove it works

Any strategy recommendation can be converted into a testable hypothesis with one click. A hypothesis captures four elements:

• Baseline metrics — your current SOV, visibility, citation rate, and sentiment at the moment of creation
• Expected change — which metric should improve and by how much
• Tasks — interactive checklist of implementation steps you track as you complete them
• Verification date — default 21 days, adjustable based on expected impact timeline

After the next monitoring cycle, the system automatically compares your before-and-after numbers. You get a clear verdict: Confirmed (statistically significant improvement), Refuted (no improvement or decline), Too Early (not enough data yet), or Neutral (no significant change after 30+ days).

No gut feeling. No “I think it’s working.” Statistical proof based on real monitoring data. Each completed hypothesis becomes part of your track record — a documented history of what actually moves AI visibility in your market.

Full pipeline: monitor → analyze → act → verify

Strategy and source analysis connect monitoring data to concrete actions. The pipeline works in a loop: monitoring reveals your position, source analysis shows where competitors publish, strategy generates an action plan, and hypothesis testing confirms whether the plan worked. Then you monitor again.

Each step builds on the previous one. Source analysis uses your monitoring data to find relevant platforms. Strategy uses source analysis results to prioritize actions. Hypotheses use strategy recommendations as implementation guides. Verification uses the next monitoring run as the measurement.

The full cycle — source analysis ($3) + strategy ($2) + monitoring ($12) — costs $17 and gives you a data-driven plan, not a guess.