NiubiGEO: Open-Source AI Brand Visibility Platform — Monitor Your Generative AI Presence 2026

Introduction

In the age of generative AI, brand visibility has taken on a new meaning. It’s not just about ranking on Google—it’s about appearing in AI-generated responses. When someone asks ChatGPT about your product category, does your brand show up?

NiubiGEO answers this question. It’s an open-source platform for monitoring AI brand visibility—tracking how often your brand appears in LLM outputs, analyzing sentiment, and generating actionable reports.

The problem it solves is real and growing. As AI assistants become primary information sources, brands that don’t monitor their AI presence are invisible to a growing audience. NiubiGEO gives you the tools to see what AI sees—and optimize accordingly.

What Is NiubiGEO?

NiubiGEO (short for “Niubi” + “GEO” = Generative Engine Optimization) is a self-hosted platform that monitors your brand’s visibility across multiple LLMs.

Key features:

• Multi-LLM monitoring — Track presence in ChatGPT, Claude, Gemini, and others
• Sentiment analysis — Understand how your brand is portrayed
• Competitive analysis — See who dominates your category
• Report generation — Export findings for stakeholders
• Self-hosted — Full data control, no vendor lock-in
• API access — Integrate with your existing workflows

┌─────────────────────────────────────────────────────┐
│                 NiubiGEO Architecture                │
├─────────────────────────────────────────────────────┤
│                                                     │
│  Brand Queries ──→ LLM Sampling Engine ──→ Results  │
│      ↓                      ↓                      │
│  (your keywords)    (multiple LLMs)          Extraction & Analysis    │
│                                              ↓                    │
│                                        Mention Detection          │
│                                        Sentiment Scoring          │
│                                        Competitive Analysis       │
│                                              ↓                    │
│                                        Report Generation          │
│                                        (PDF, CSV, Dashboard)      │
│                                                     │
└─────────────────────────────────────────────────────┘

The GEO Problem

Traditional SEO optimizes for search engines. GEO optimizes for AI assistants. The challenges are different:

NiubiGEO bridges this gap by providing systematic monitoring of AI visibility.

Installation & Setup

Quick Start

# Clone the repository
git clone https://github.com/Albert-Weasker/niubigeo.git
cd niubigeo

# Install dependencies
npm install

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Run setup
npm run setup

# Start the application
npm start

Docker Deployment

FROM node:20-slim

WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

COPY . .
RUN npm run build

EXPOSE 3000
CMD ["npm", "start"]

# Build and run
docker build -t niubigeo .
docker run -p 3000:3000 --env-file .env niubigeo

Environment Configuration

# API Keys for LLM providers
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AIza...

# Database
DATABASE_URL=postgresql://user:pass@localhost/niubigeo

# Redis for caching
REDIS_URL=redis://localhost:6379

# Application
PORT=3000
NODE_ENV=production

Core Features

Brand Monitoring

Monitor your brand across multiple LLMs with configurable queries.

from niubigeo import BrandMonitor

monitor = BrandMonitor()

# Define brands to track
brands = [
    {"name": "YourBrand", "keywords": ["yourbrand", "your-brand"]},
    {"name": "CompetitorA", "keywords": ["competitor-a"]},
    {"name": "CompetitorB", "keywords": ["competitor-b"]}
]

# Run monitoring
results = monitor.run(brands=brands, llms=["gpt-4", "claude-3", "gemini"])

# Analyze results
for brand in results:
    print(f"{brand.name}:")
    print(f"  Mentions: {brand.mention_count}")
    print(f"  Sentiment: {brand.avg_sentiment:.2f}")
    print(f"  Visibility score: {brand.visibility_score:.1%}")

Sentiment Analysis

Understand how your brand is portrayed in AI responses.

from niubigeo import SentimentAnalyzer

analyzer = SentimentAnalyzer(model="microsoft/deberta-v3-large")

# Analyze responses
sentiment_results = analyzer.analyze(responses)

for result in sentiment_results:
    print(f"Text: {result.text[:50]}...")
    print(f"Sentiment: {result.label} ({result.confidence:.2%})")
    print(f"Scores: positive={result.positive:.2%}, neutral={result.neutral:.2%}, negative={result.negative:.2%}")

Competitive Analysis

See how you compare against competitors in AI responses.

from niubigeo import CompetitiveAnalyzer

analyzer = CompetitiveAnalyzer()

# Define category queries
queries = [
    "best AI coding assistant",
    "top project management tools",
    "best CRM for small business"
]

# Analyze competition
results = analyzer.analyze(queries=queries, brands=your_brands)

# Get share of voice
for query in results:
    print(f"\n{query}:")
    for brand in query.brands:
        print(f"  {brand.name}: {brand.share_of_voice:.1%} visibility")

Report Generation

Export findings in multiple formats.

from niubigeo import ReportGenerator

generator = ReportGenerator()

# Generate PDF report
pdf_path = generator.generate_pdf(
    results=monitoring_results,
    title="Q3 2026 Brand Visibility Report",
    output_dir="./reports"
)

# Generate dashboard
dashboard_path = generator.generate_dashboard(
    results=monitoring_results,
    output_dir="./dashboards"
)

# Export CSV for further analysis
csv_path = generator.export_csv(
    results=monitoring_results,
    filename="brand_mentions_2026.csv"
)

Integration Patterns

Webhook Integration

Receive real-time alerts when your brand appears in LLM responses.

from niubigeo import WebhookClient

webhook = WebhookClient(
    endpoint="https://your-webhook.com/alerts",
    threshold=0.8  # Alert on high-confidence mentions
)

# Monitor and alert
monitor = BrandMonitor()
results = monitor.run(
    brands=your_brands,
    on_mention=webhook.send,
    on_sentiment_change=webhook.send
)

Database Integration

Store results for historical analysis.

from niubigeo import DatabaseConnector
from sqlalchemy import create_engine

# Connect to your database
db = DatabaseConnector(create_engine("postgresql://user:pass@localhost/niubigeo"))

# Save monitoring results
db.save_results(monitoring_results)

# Query historical data
historical = db.query_historical(
    brand="YourBrand",
    start_date="2026-01-01",
    end_date="2026-09-24"
)

API Integration

Use NiubiGEO’s API in your own applications.

import requests

# Query NiubiGEO API
response = requests.get(
    "http://localhost:3000/api/v1/monitor",
    headers={"Authorization": "Bearer your-token"},
    params={"brand": "YourBrand", "date": "2026-09-24"}
)

data = response.json()
print(f"Mentions: {data['mention_count']}")
print(f"Sentiment: {data['avg_sentiment']}")

Benchmarks & Performance

Monitoring Speed

Performance varies based on LLM response times and rate limits.

Cost Estimation

# Monthly cost calculator
def calculate_monthly_cost(query_count, llm_costs):
    total = 0
    for llm, cost_per_query in llm_costs.items():
        total += query_count * cost_per_query
    return total

# Example: 10,000 queries/month across 3 LLMs
monthly_cost = calculate_monthly_cost(10000, {
    "gpt-4": 0.03,      # $0.03 per query
    "claude-3": 0.025,  # $0.025 per query
    "gemini": 0.02      # $0.02 per query
})

print(f"Monthly cost: ${monthly_cost:.2f}")

Accuracy Metrics

Comparison with Alternatives

When to choose NiubiGEO: When you need AI-specific monitoring, full data control, and cost efficiency. It’s ideal for startups and mid-market companies that can’t afford enterprise pricing.

When to skip NiubiGEO: If you only need social media monitoring, established tools like Brandwatch may have more features. For enterprise-scale monitoring with dedicated support, consider commercial alternatives.

Limitations & Honest Assessment

What NiubiGEO Doesn’t Do

• It doesn’t optimize your AI presence. NiubiGEO monitors visibility but doesn’t provide optimization recommendations. That requires separate GEO strategy.
• It doesn’t cover all LLMs. Currently supports major providers, but niche or regional models may not be included.
• It doesn’t predict future rankings. AI model training is opaque; NiubiGEO can only measure current visibility.

Known Limitations

1. LLM variability: Different model versions may produce different responses for the same query. Monitor across versions for stability.

2. Prompt sensitivity: Small prompt changes can affect which brands appear. Use consistent prompting for accurate tracking.

3. Cost scaling: Monitoring many brands or queries scales linearly in cost. Plan budget accordingly.

When to Be Cautious

NiubiGEO measures what LLMs say, not what users believe. A brand might appear frequently in AI responses but have negative sentiment. Always combine monitoring with sentiment analysis for actionable insights.

Also, remember that AI models are trained on historical data. Your current visibility may not predict future rankings after model updates.

Frequently Asked Questions

Q1: How often should I run monitoring?

Weekly monitoring is recommended for active brands. Monthly is sufficient for passive tracking. Run more frequently during product launches or marketing campaigns.

Q2: Can I monitor competitor brands?

Yes. Add competitor keywords to your monitoring configuration. This helps you understand competitive positioning in AI responses.

Q3: Is the sentiment analysis accurate?

NiubiGEO uses state-of-the-art sentiment models (DeBERTa-v3), achieving ~92% accuracy on standard benchmarks. However, AI-generated text can contain nuance that challenges sentiment classifiers.

Q4: Can I use NiubiGEO for non-English brands?

Yes. The platform supports multilingual monitoring. Ensure you provide keywords in the target language.

Q5: How does NiubiGEO handle rate limits?

The built-in rate limiter respects provider limits and backs off on 429 errors. Configure appropriate delays in your environment.

Q6: Can I export data to other tools?

Yes. NiubiGEO supports CSV, JSON, and API export. Integrate with your BI tools or dashboards.

Q7: Is there a cloud-hosted option?

Currently self-hosted only. A managed cloud version may be available in the future—check the GitHub releases.

Conclusion

NiubiGEO addresses a growing need: understanding your brand’s presence in AI-generated responses. As generative AI becomes a primary information source, brands that ignore AI visibility risk invisibility to a growing audience.

The platform’s strength lies in its openness. Self-hosted deployment means full data control. Configurable monitoring means you track what matters. Competitive analysis features mean you understand your position.

For teams building GEO strategies or monitoring AI brand presence, NiubiGEO offers a practical, cost-effective solution. It won’t replace enterprise tools for large-scale operations, but for most organizations, it provides everything needed to understand and improve AI visibility.

The future of brand visibility includes AI. NiubiGEO helps you see that future clearly.

Try NiubiGEO: https://github.com/Albert-Weasker/niubigeo

Join the discussion: https://t.me/DIBI8_Group

Related reading: GEO Strategy Guide • AI Visibility Metrics

Sources & Further Reading:

• Official repo: https://github.com/Albert-Weasker/niubigeo
• Documentation: https://niubigeo.dev/docs
• GEO research: https://arxiv.org/abs/2406.xxxxx
• Sentiment analysis benchmarks: https://huggingface.co/microsoft/deberta-v3-large