Building an AI Visibility Monitoring System: Technical Architecture
A comprehensive technical guide to building a production-ready AI visibility monitoring system that tracks brand mentions across ChatGPT, Perplexity, Claude, and other LLM platforms.
As AI-powered search becomes the primary discovery mechanism for consumers, monitoring your brand's visibility across Large Language Models (LLMs) is critical. This guide provides a technical deep-dive into building a scalable, production-ready AI visibility monitoring system that tracks brand mentions, calculates share of voice, and provides actionable competitive intelligence.
System Architecture Overview
A robust AI visibility monitoring system requires a multi-layered architecture that handles concurrent API requests, processes large volumes of data, and provides real-time analytics. The architecture consists of four primary layers:
- API Gateway Layer: Handles authentication, rate limiting, and request routing
- Data Collection Layer: Manages LLM API integrations and response parsing
- Analytics Engine: Processes data for share of voice, sentiment, and competitive analysis
- Data Storage & Caching: Stores results and caches responses for efficiency
Architecture Components
Key Features:
- Asynchronous request processing with async/await
- JWT-based authentication and API key management
- Rate limiting per client and per LLM provider
- Request queuing and load balancing
- Request/response logging and tracing
Code Example:
from fastapi import FastAPI, HTTPException, Depends
from fastapi.middleware.cors import CORSMiddleware
from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.util import get_remote_address
import asyncio
app = FastAPI(title="AI Visibility Monitoring API")
limiter = Limiter(key_func=get_remote_address)
@app.post("/api/v1/monitor/brand")
@limiter.limit("100/minute")
async def monitor_brand(
request: BrandMonitorRequest,
current_user: User = Depends(get_current_user)
):
"""Monitor brand mentions across multiple LLM platforms"""
tasks = [
query_chatgpt(request.brand, request.prompts),
query_perplexity(request.brand, request.prompts),
query_claude(request.brand, request.prompts),
]
results = await asyncio.gather(*tasks, return_exceptions=True)
return aggregate_results(results)Key Features:
- Multi-provider LLM API integration (OpenAI, Anthropic, Perplexity)
- Asynchronous batch query processing
- Response parsing and entity extraction
- Data normalization across different response formats
- Error handling and retry logic with exponential backoff
Code Example:
import aiohttp
from typing import List, Dict, Optional
import asyncio
class LLMQueryEngine:
def __init__(self):
self.session = aiohttp.ClientSession()
self.retry_config = {
'max_retries': 3,
'backoff_factor': 2,
'timeout': 30
}
async def query_multiple_llms(
self,
brand: str,
prompts: List[str]
) -> Dict[str, List[Dict]]:
"""Query multiple LLM platforms concurrently"""
tasks = {
'chatgpt': self._query_openai(brand, prompts),
'perplexity': self._query_perplexity(brand, prompts),
'claude': self._query_claude(brand, prompts),
}
results = await asyncio.gather(*tasks.values(), return_exceptions=True)
return dict(zip(tasks.keys(), results))
async def _query_openai(
self,
brand: str,
prompts: List[str]
) -> List[Dict]:
"""Query OpenAI ChatGPT API"""
async with self.session.post(
'https://api.openai.com/v1/chat/completions',
headers={'Authorization': f'Bearer {OPENAI_API_KEY}'},
json={
'model': 'gpt-4',
'messages': [{'role': 'user', 'content': p} for p in prompts],
'temperature': 0.7
}
) as response:
data = await response.json()
return self._extract_mentions(data, brand)Key Features:
- Share of voice (SOV) calculation algorithms
- Sentiment analysis using transformer models
- Citation extraction and source tracking
- Competitive benchmarking and gap analysis
- Time-series trend analysis
Code Example:
from collections import Counter
from typing import Dict, List
import statistics
class AnalyticsEngine:
def calculate_share_of_voice(
self,
brand_mentions: int,
competitor_mentions: Dict[str, int]
) -> float:
"""Calculate share of voice percentage"""
total_mentions = brand_mentions + sum(competitor_mentions.values())
if total_mentions == 0:
return 0.0
return (brand_mentions / total_mentions) * 100
def analyze_sentiment(
self,
mentions: List[str]
) -> Dict[str, float]:
"""Analyze sentiment distribution"""
from transformers import pipeline
sentiment_analyzer = pipeline(
"sentiment-analysis",
model="cardiffnlp/twitter-roberta-base-sentiment"
)
results = sentiment_analyzer(mentions)
sentiment_counts = Counter([r['label'] for r in results])
total = len(results)
return {
'positive': sentiment_counts.get('POSITIVE', 0) / total,
'negative': sentiment_counts.get('NEGATIVE', 0) / total,
'neutral': sentiment_counts.get('NEUTRAL', 0) / total
}
def extract_citations(
self,
responses: List[str]
) -> List[Dict]:
"""Extract citation sources from LLM responses"""
import re
citations = []
url_pattern = re.compile(
r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
)
for response in responses:
urls = url_pattern.findall(response)
citations.extend([{'url': url, 'source': 'llm_response'} for url in urls])
return citationsKey Features:
- PostgreSQL for structured data storage
- Redis for caching and session management
- Vector database (Pinecone/Weaviate) for semantic search
- Time-series database for metrics storage
- Data retention and archival policies
Code Example:
from sqlalchemy import create_engine, Column, Integer, String, DateTime, JSON
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
import redis
from datetime import datetime, timedelta
Base = declarative_base()
class BrandMention(Base):
__tablename__ = 'brand_mentions'
id = Column(Integer, primary_key=True)
brand = Column(String, index=True)
platform = Column(String, index=True)
prompt = Column(String)
response = Column(JSON)
mentions_count = Column(Integer)
sentiment = Column(String)
citations = Column(JSON)
timestamp = Column(DateTime, default=datetime.utcnow, index=True)
class DataStore:
def __init__(self):
self.engine = create_engine(DATABASE_URL)
self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
Base.metadata.create_all(self.engine)
async def cache_query_result(
self,
cache_key: str,
result: Dict,
ttl: int = 3600
):
"""Cache query results to reduce API calls"""
import json
self.redis_client.setex(
cache_key,
ttl,
json.dumps(result)
)
async def store_mention(
self,
mention: BrandMention
):
"""Store brand mention in database"""
Session = sessionmaker(bind=self.engine)
session = Session()
session.add(mention)
session.commit()
session.close()Technology Stack
The following technology stack provides the foundation for a scalable, maintainable system:
- FastAPI
- Python 3.11+
- AsyncIO
- Pydantic
- OpenAI API
- Anthropic Claude API
- Perplexity API
- Google Gemini API
- PostgreSQL
- Redis
- Pinecone/Weaviate
- TimescaleDB
- NumPy
- Pandas
- Transformers (Hugging Face)
- scikit-learn
- Prometheus
- Grafana
- Sentry
- OpenTelemetry
- Docker
- Kubernetes
- AWS/GCP/Azure
- Terraform
Implementation Best Practices
1. Asynchronous Processing
Use Python's asyncio and aiohttpfor concurrent API requests. This allows you to query multiple LLM platforms simultaneously, significantly reducing total query time.
2. Rate Limiting & Retry Logic
Implement exponential backoff retry logic to handle API rate limits gracefully. Use Redis to track rate limits per provider and implement circuit breakers to prevent cascading failures.
3. Data Normalization
Different LLM providers return responses in varying formats. Create a unified data model that normalizes responses across all platforms, making analytics consistent and reliable.
4. Caching Strategy
Cache query results for identical prompts to reduce API costs and improve response times. Use Redis with appropriate TTL values based on how frequently you need fresh data.
5. Monitoring & Observability
Implement comprehensive logging, metrics collection, and distributed tracing. Use tools like Prometheus for metrics, Grafana for visualization, and Sentry for error tracking.
Deployment Considerations
For production deployment, consider the following:
- Containerization: Use Docker for consistent deployments across environments
- Orchestration: Kubernetes for auto-scaling and high availability
- Database Scaling: Use read replicas for analytics queries and connection pooling
- API Gateway: Implement an API gateway (Kong, AWS API Gateway) for additional security and rate limiting
- CDN: Use a CDN for serving cached analytics dashboards and reports
Performance Optimization
To handle thousands of queries daily across multiple platforms:
- Implement connection pooling for database and Redis connections
- Use batch processing for bulk queries to reduce API overhead
- Implement query result pagination for large datasets
- Use background workers (Celery, RQ) for long-running analytics jobs
- Optimize database queries with proper indexing on brand, platform, and timestamp columns
Security Considerations
Security is critical when handling brand monitoring data:
- Encrypt API keys and sensitive data at rest and in transit
- Implement role-based access control (RBAC) for different user permissions
- Use JWT tokens with short expiration times for API authentication
- Implement request signing to prevent replay attacks
- Regular security audits and dependency updates
Ready to Build Your AI Visibility System?
Elatify's AI Visibility Agent provides enterprise-grade brand monitoring across all major LLM platforms. Get started with our comprehensive solution or build your own using these technical guidelines.