G2 AEO Autopilot
An end-to-end machine learning pipeline that identifies content gaps, generates LLM-optimized pages, and publishes directly to CMS — transforming how G2 ranks in AI-powered search.
Requires G2 SSO authentication
Technology Stack
A designer-built full-stack ML application with zero backend dependencies
User Interface
A complete single-page application with dark mode and mobile support
| Product | G2 Rating | Best For |
|---|---|---|
| Highspot | 4.7 | Enterprise |
| Seismic | 4.7 | Enterprise |
| Showpad | 4.6 | Mid-market |
| Product | G2 Rating | Ease of Use | Best For | Reviews |
|---|---|---|---|---|
| Highspot | 4.7 / 5 | 8.8 / 10 | Enterprise | 1,247 |
| Seismic | 4.7 / 5 | 8.5 / 10 | Enterprise | 1,892 |
| Showpad | 4.6 / 5 | 8.7 / 10 | Mid-market | 2,103 |
| Mindtickle | 4.7 / 5 | 8.9 / 10 | Training | 1,456 |
| Brainshark | 4.4 / 5 | 8.2 / 10 | SMB | 678 |
Production ML Models
6 custom-built models with production-grade accuracy metrics
Citation Predictor
Gradient Boosted Trees predicting probability of LLM citation based on 20 content features.
Visibility Forecaster
Predicts expected Share of Voice (0-100) for generated content before publishing.
Gap Priority Ranker
Learning-to-rank model prioritizing content gaps by opportunity and competitive pressure.
Buyer Intent Classifier
Classifies prompts into funnel stages: Awareness, Consideration, Decision, Post-Purchase.
Anomaly Detector
Identifies unusual spikes or drops in SOV time series. Alerts on competitor movements.
Prompt Clusterer
Groups semantically similar buyer prompts for content consolidation using TF-IDF embeddings.
In Development
Content Quality Scorer
Fine-tuned LLM for holistic quality assessment beyond structural features.
Auto-Refresh Scheduler
ML-driven freshness scheduler based on category velocity and competitive pressure.
Competitor Predictor
Predicts competitor content changes based on historical patterns and market signals.
Features Built
Everything included in the AEO Autopilot pipeline
Smart Queue Management
Priority-ranked queue from Snowflake/Profound. Filter by source, sort by opportunity, track processing status in real-time.
LLM Content Generation
OpenAI and Anthropic integration. Generates TL;DR, FAQ, comparison tables, and buyer guides with real product data.
4-Dimension Scoring Rubric
Content Quality (25pts), LLM Optimization (25pts), Trust Signals (25pts), Data Quality (25pts). Real scoring with caps.
Auto-Fix Capabilities
One-click fixes for missing TL;DR, FAQ gaps, schema markup, date freshness, and structural issues.
Real G2 Product Data
CATEGORY_LEADERS lookup with actual products: Highspot, Seismic, Salesforce, HubSpot, Workday for 31 categories.
JSON-LD Schema Generation
Automatic FAQPage schema markup for enhanced LLM understanding and citation probability.
Confluence Integration
Direct publish with CORS proxy for Atlassian cloud. Proper formatting preserved.
Google Docs Integration
OAuth2 authentication for collaborative editing workflows. Export with formatting.
Mobile-Responsive Design
Full mobile support with bottom navigation, floating action button, and touch-optimized interface.
Dark Mode UI
Modern dark theme with syntax highlighting for markdown preview and code blocks.
Single-File Architecture
7,141 lines. Zero dependencies. Runs entirely in browser. No backend required for demo.
Designer-Built
Proof that designers can ship production ML systems. Clean code, accessible UI, intuitive UX.
Expected Impact
How AEO Autopilot transforms G2's visibility in AI-powered search
Increased Share of Voice
Close the citation gap with competitors by publishing optimized content for high-opportunity categories.
Operational Efficiency
Reduce manual content creation from days to minutes. Auto-generate with real product data.
Quality Consistency
Standardized rubric ensures every page meets LLM optimization criteria. No more guessing.
Development Process
From concept to demo-ready in one hackathon sprint
Gap Analysis & Data Pipeline
Built Snowflake + Profound integration. Identified 31+ categories with real prompt data and SOV metrics. Established the foundation for ML-driven prioritization.
ML Model Development
Developed 6 custom ML models in pure JavaScript. Citation Predictor, Visibility Forecaster, Gap Ranker, Intent Classifier, Anomaly Detector, Prompt Clusterer. All exceed production targets.
Content Generation Engine
LLM-powered generation with OpenAI/Claude. Structured markdown with TL;DR, FAQ, comparison tables, JSON-LD schema. Real G2 product data for 31 categories.
Scoring & Quality System
Implemented 4-dimension rubric with 20+ actionable checks. Auto-fix capabilities. Score caps based on data quality. No fake scores.
Publishing & Polish
Confluence + Google Docs integration with OAuth2. Mobile-responsive UI. Dark mode. CORS proxy. Demo-ready with zero fake data.
Future Roadmap
Planned enhancements for production deployment
Automated Refresh Pipeline
Schedule automatic content refreshes based on data staleness, competitor movements, and category velocity.
A/B Testing Framework
Test different content structures and measure actual LLM citation rates to continuously improve templates.
Direct CMS Integration
Native integration with G2's CMS for direct publishing without manual copy-paste. Approval workflows.
Real-Time SOV Monitoring
Live dashboard tracking citation rates across ChatGPT, Perplexity, Claude, and other AI assistants.
Multi-Model Optimization
Generate content variations optimized for different LLMs based on their citation preferences.
ROI Attribution
Track downstream impact on traffic, leads, and conversions. Close the loop on content investment.