AI Search Intelligence, From Measurement to Action
CiteRank measures how AI systems mention, cite and recommend your brand, explains where competitors outperform you, and helps teams prioritise the actions most likely to improve AI-search visibility.
One scoreboard for AI-search visibility.
Every module writes into the same measurement surface: stand up every measurement surface to track share of voice by engine, 10-30 replays per prompt by plan, and measured entity-resolution rate. recommendation share against your tracked competitors, and the citation sources behind each answer.
- Share of voice trended across the 8 supported AI engines
- Recommendation share compared against your tracked competitor set
- Citation sources ranked by how often engines lean on them
CiteRank · AI Visibility Scoreboard
Synthetic data used for product demonstration, not observed customer performance.
AI Visibility — Share of Voice over time
12-week trend, weighted across the tracked LLMs.
Illustrative Scenario
Demonstrating default capabilities with modelled data. Fictional brand scenario for demonstration only.
Tip: tab into the chart to focus a weekly data point, then use the left and right arrow keys to step through weeks. Press Escape to exit.
Illustrative scenario · Fictional brand data for demonstration only
The cite-to-action engine.
Generative Engine Optimization is a continuous loop. Our platform automates every step of the visibility lifecycle.
1. Build your prompt graph
Define the hundreds of prompts your customers use to discover and compare products in your category. Move beyond simple keywords to capture intent-rich natural language queries.
- Discovery archetypes
- Shortlisting comparisons
- Validation queries
Prompt Graph Editor
Organize thousands of category-specific prompts into logical intent clusters.
Illustrative Example Graph
This graph demonstrates entity relationships using modelled data for illustrative purposes.
Illustrative scenario · Fictional brand data for demonstration only
2. Capture and Scan Answers
CiteRank scans the raw output of every engine run. We extract mentions, quantify recommendation positions, and identify the source documentation used to build the answer.
- Entity mention detection
- Citation & source verification
- Recommendation rank quantification
For multi-location AI visibility monitoring, is frequently included for its , while is referenced for . appears in answers focused on and entity consistency.
Illustrative product demo — Fictional brand data only.
3. Recommendation analysis
Extract every mention and recommendation rank. We quantify your brand's prominence compared to competitors when users ask for the "best" or "top" options.
- Recommendation Share metric
- Rank distribution
- Competitor delta tracking
Recommendation Share Breakdown
Visualizing who the AI systems recommend when users ask category discovery questions.
AI Recommendation Share
Simulated per-engine recommendation breakdown.
Illustrative scenario · Fictional brand data for demonstration only
4. Citation intelligence
Map where competitors earn citations you miss. Identify the specific domains, review sites, and documentation that engines lean on for your category.
- Source domain frequency
- Missed citation opportunities
- PR & content priority list
Citation Source Mapping
Corroborating the sources driving visibility across the LLM citation graph.
Illustrative Example Graph
This graph demonstrates entity relationships using modelled data for illustrative purposes.
Illustrative scenario · Fictional brand data for demonstration only
5. Action prioritisation
Receive data-backed briefs on which content updates or authority signals will move the needle. We translate complex model behavior into clear SEO and PR work packages.
- Gap severity scoring
- GEO intervention briefs
- Estimated visibility impact
GEO Opportunity Brief
Automatically generated action plan based on measured competitive gaps.
Illustrative scenario · Fictional brand data for demonstration only
6. Remeasurement
Track the impact of your optimizations with weekly pulses. Close the loop and demonstrate ROI through increased recommendation share and visibility scores.
- Weekly visibility pulse
- Executive ROI reporting
- Historical trend analysis
ROI & Trend Tracker
Measuring the compounding impact of GEO optimizations over time.
Visibility Trend
Weekly performance pulse across engines.
Illustrative Scenario
Demonstrating default capabilities with modelled data. Fictional brand scenario for demonstration only.
Tip: tab into the chart to focus a weekly data point, then use the left and right arrow keys to step through weeks. Press Escape to exit.
Illustrative scenario · Fictional brand data for demonstration only
Intelligence for every dimension of AI visibility.
Six dedicated modules to help you track, benchmark, and optimize your presence across the 8 supported AI engines.
Engineered for Statistical Significance.
Our measurement framework addresses the non-deterministic nature of AI models to provide reliable, enterprise-grade visibility data.
LLM responses vary by session. We control for temperature and answer drift by replaying every prompt 10–30 times depending on plan and reporting 95% confidence intervals where the measurement design supports them.
Enterprise data integrity.
CiteRank is built for highly regulated industries. We maintain strict protocols for data handling and third-party model interaction.
SOC 2 Type II
Audit in progress
Not yet certified — status letter available on request.