Patent-Pending AI Visibility IntelligenceNeuroRank® Pricing: From $7 to Enterprise
NeuroRank is a patent-pending AI visibility intelligence platform that diagnoses how ChatGPT, Gemini, Claude, and Perplexity represent your brand. Start with a $7 Live Forensic Audit. Scale into Model Preference Engineering from $225/mo when you are ready.
Stress tested across 150+ brands in 65 industries. Validated through leadership interviews across Asia, Europe, Middle East, USA, and North America.
Live Forensic Audit
For CMOs who need to see the problem before committing.
See exactly how AI models perceive, misrepresent, and omit your brand. Four AI engines. Ten intelligence sections. One unified report.
- 10-section intelligence report across ChatGPT, Gemini (includes AI overviews), Claude, Perplexity + Combined synthesis
- Aided and unaided recall analysis: The brand health techniques the ad industry uses, applied to AI
- Competitive scoring across 5 competitors on 6 brand, content and technical dimensions
- Hallucination and gap detection with ORHL classification
- Top sources cited by AI identified with actual URLs
- Fresh-token execution: Every result reflects what a new user would see
- Brand Battle Cards: Exportable competitive matrix for leadership
- Deep Insights: Conversational AI interface across all audit data
Model Preference Engineering Growth
For marketing teams ready to fix their AI visibility every month.
Continuous AI visibility governance. 5,500+ prompt runs per cluster. Every source traced. Every gap prescribed. Every month tracked.
Select LLM Models
Select Prompt Clusters
Model Preference Engineering Enterprise
For global brands that need NeuroRank to run the program across markets.
Full AI visibility governance. Per brand. Per region. Strategy roadmap. Best practices and playbooks. Maker-Checker governance.
Most AI visibility tools monitor. NeuroRank diagnoses, prescribes, and tracks. From $7.
Stress-tested across 150+ brands in 65 industries. Validated through leadership interviews across Asia, Europe, Middle East, USA, and North America. ISO/IEC 27001 certified.
Ambika Sharma
Founder, Chief Strategist at Pulp Strategy Communications and Product Architect of NeuroRank.
Feature Comparison
Forensic Intelligence (all tiers)
| Feature | Live Forensic Audit | MPE Growth | MPE Enterprise |
|---|---|---|---|
| 10-section intelligence report | |||
| Detailed Keyword research, | |||
| 100+ prompts, with customer intent | |||
| 4 LLMs + Combined synthesis | 2-4 + Combined | 2-4 + Combined | |
| Aided/unaided recall analysis | |||
| Competitive scoring (5 competitors, 6 dimensions) | ✓ Extensive | ✓ Extensive | |
| ORHL gap classification | |||
| Source identification with actual URLs | Few | ✓ Extensive | ✓ Extensive |
| Brand Battle Card | |||
| Content + Technical Visibility audit | ✓ Extensive | ✓ Extensive | |
| Improvement Recommendations | ✓ Detailed by prompt cluster | ✓ Detailed by prompt Cluster | |
| Deep Insights conversational interface | |||
| Dashboard + exportable report |
Monthly Visibility Intelligence (Growth + Enterprise)
| Feature | Live Forensic Audit | MPE Growth | MPE Enterprise |
|---|---|---|---|
| 5,500+ fresh-token prompt runs per cluster | — | ||
| Source links identified, read, catalogued per prompt | — | ✓ Extensive | ✓ Extensive |
| Citation link auditing | — | ✓ Extensive | ✓ Extensive |
| Citation tracking: which pages/assets cited, how often, by which model | — | ✓ Extensive | ✓ Extensive |
| Citation inclusion growth: MoM citation footprint expansion/contraction | — | ✓ Extensive | ✓ Extensive |
| Prompt Inclusion Score (MoM) | — | ✓ Extensive | ✓ Extensive |
| Trust Recall tracking | — | ||
| Hallucination Rate monitoring | One time |
Detailed Competitive Intelligence (Growth + Enterprise)
| Feature | Live Forensic Audit | MPE Growth | MPE Enterprise |
|---|---|---|---|
| Competitive displacement monitoring (per model, per prompt) | — | ||
| Dynamic GEO tracking: real-time competitor movement | — | ||
| Per-model agent intelligence + bias flags | — | ||
| Latent space mapping: hidden AI preference logic | — | ||
| Competitor citation comparison: whose sources AI prefers | — | ||
| Competitor inclusion rate benchmarking | — |
Prescriptions, Guidance, and Playbooks (Growth + Enterprise)
| Feature | Live Forensic Audit | MPE Growth | MPE Enterprise |
|---|---|---|---|
| Prioritized Recommendation Engine | — | ||
| Source-linked prescriptions with URLs | — | ||
| RAG layer optimization guidance | — | ||
| Content + technical guidance (what, where, schema) | — | ||
| Best practices and structured playbooks | — | ||
| MoM implementation tracking (did fixes work?) | — | ||
| Per-prompt optimization recommendations | — | ||
| Keyword intelligence (search to AI bridge) | — | ||
| Prompt Conditioning Loop (patent-pending) | — | ||
| Memory acceleration | — |
Enterprise Additions
| Feature | Live Forensic Audit | MPE Growth | MPE Enterprise |
|---|---|---|---|
| Per brand, per region tracking | — | Single Brand | Multi brand / Multi Region |
| Multi-market setups (regional AI variance) | — | — | |
| Strategy roadmap (per market) | — | — | |
| Best practices and playbooks | — | — | |
| Maker-Checker governance | — | — | |
| Risk mitigation + conquesting alerts | — | — | |
| Inclusion benchmarking (quarterly) | — | — | |
| Team enablement + stakeholder summaries | — | — | |
| Dedicated account management | — | — | |
| Team / role-based logins | — | Add additional seats | ✓ Custom |
| Email Support | — |
Platform and Billing
| Feature | Live Forensic Audit | MPE Growth | MPE Enterprise |
|---|---|---|---|
| Brands | 1 | 1 | Per brand, per region |
| Prompt clusters | — | 1+ (cumulative ramp) | Custom |
| Seats | 1 | 1 (add more) | Custom |
| Billing | One-time $7 | Monthly / 6mo / Annual | Monthly / 6mo / Annual |
| Cancel policy | N/A | Anytime, end of month | Anytime, end of month |
| Dashboard | |||
| Exportable reports |
AI models are being updated continuously. Every month without visibility data is a month your competitors are conditioning models without you knowing.
How Model Preference Engineering Works
Model Preference Engineering is a gate-controlled, sequential six-step monthly pipeline. It requires the Live Forensic Audit to run first. Your audit findings (visibility gaps, content roadmap, competitive matrix) feed directly into MPE as inputs.
Keyword Intelligence
Retrieves current top Google search keywords + volumes for your brand and competitors. Bridges traditional SEO to the AI prompt landscape.
Prompt Cluster
Uses aided, unaided recall advertising methodologies, captures proactive recommendations from LLM’s, captures detailed information across thousands of fresh token runs of live prompts, across the region. Large data analysis: Hero Prompt + Sub-prompts organized by consumer intent (Brand, Product, Category, Purchase-Intent). Built cumulatively month on month.
Live Prompt Intelligence
Large volume runs per individual prompt using fresh authentication tokens. Multi-location, multi-model parallel execution. Every source link cited by AI is identified, read, and catalogued.
Source Search and Probe
Reads all cited sources, cumulates and maps against trust signals in LLM’s, identifies trust signals, pinpoints biases and negative perceptions across trusted sources by industry. Maps recommendations.
Recommendation Engine
Identifies every visibility gap. Generates prioritized recommendations: What to fix, why, with source URLs and citation chains. Tracks month-on-month implementation vs inclusion growth status.
Agent Intelligence
Per-model visibility analysis: How Claude vs. ChatGPT vs. Gemini vs. Perplexity each represent your brand. Model-specific bias flags. Month-on-month trend per model.
Rag Influence
Agent swarms use fresh token methodology with 5500+ runs across region per prompt cluster, influencing the RAG memory layer and accelerating inclusion.
Overall Visibility Report
Headline visibility score across all models, clusters, prompts, and locations. Breakdowns by LLM, cluster, region, prompt type. Competitor comparison. Monthly trend chart.
Cumulative monthly architecture: One or more new cluster is added each month. All previous clusters are re-run at varied intensities each month. Month 3 runs 3 clusters. Month 12 runs 12 clusters. The longitudinal dataset grows in richness over time.
Note: NeuroRank provides detailed guidance, execution strategy, source links, citation chains, tracking, best practices, and playbooks. It does not include content writing, publishing, or technical implementation at any tier. Your team or agency implements. NeuroRank tells you exactly what to do and gives you the structured playbooks to do it.