How we built an AI-powered competitive intelligence system that transforms weeks of manual research into minutes of automated analysis using Strands agents and Bright Data's enterprise web scraping capabilities.
Introduction
In today's fast-paced business environment, competitive intelligence can make or break strategic decisions. Traditional approaches involve manual research, scattered data sources, and weeks of analysis. What if we could automate this entire process using AI agents that work together seamlessly?
This article walks through building a Multi-Agent Competitive Intelligence Platform that combines:
- 🤖 Strands Agents for autonomous AI workflows
- 🌐 Bright Data for enterprise-grade web scraping
- 🧠 Google Gemini 2.0 for advanced analysis
- ⚡ FastAPI with real-time streaming
- 🎨 React + TypeScript frontend with live updates
The Challenge
Traditional competitive intelligence faces several challenges:
- Manual Data Collection: Hours spent browsing websites, collecting pricing info, and researching leadership teams
- Fragmented Analysis: Data scattered across multiple sources without unified insights
- Outdated Reports: By the time analysis is complete, information may already be stale
- Limited Scalability: Human analysts can only research a few competitors at a time
Our Solution: Multi-Agent Architecture
We designed a three-agent workflow that mirrors how human analysts work:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ 📊 Researcher │───▶│ 🔍 Analyst │───▶│ 📝 Writer │
│ Agent │ │ Agent │ │ Agent │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
Data Collection Strategic Analysis Report Generation
- Web scraping - SWOT analysis - Executive summary
- Market research - Threat assessment - Recommendations
- Company intel - Competitive position - Action items
Technical Implementation
1. Setting Up the Environment
First, let's set up our dependencies:
# requirements.txt
fastapi>=0.104.0
uvicorn>=0.24.0
strands>=0.1.0
strands-tools>=0.1.0
litellm>=1.0.0
pydantic>=2.0.0
# Environment variables
export GEMINI_API_KEY="your_gemini_api_key"
export BRIGHTDATA_API_KEY="your_brightdata_api_key"
2. Creating Specialized Agents
Each agent has a specific role and prompt optimized for its task:
from strands import Agent
from strands.models.litellm import LiteLLMModel
from strands_tools import bright_data
def get_gemini_model():
"""Configure Gemini model using LiteLLM"""
gemini_api_key = os.getenv("GEMINI_API_KEY")
return LiteLLMModel(
client_args={"api_key": gemini_api_key},
model_id="gemini/gemini-2.0-flash",
params={"max_tokens": 4000, "temperature": 0.3}
)
# Researcher Agent - Data Collection
RESEARCHER_PROMPT = """You are a Researcher Agent specialized in competitive intelligence data gathering.
Your role:
1. **Data Collection**: Use bright_data tool to gather comprehensive competitor information
2. **Source Discovery**: Find recent news, funding announcements, product launches
3. **Website Analysis**: Scrape pricing pages, product descriptions, company information
4. **LinkedIn Intelligence**: Get structured data about leadership and team size
Focus on recent developments, pricing strategy, leadership team, and market position.
Keep findings under 800 words and include source URLs."""
# Analyst Agent - Strategic Analysis
ANALYST_PROMPT = """You are an Analyst Agent specialized in competitive intelligence analysis.
Your role:
1. **Strategic Analysis**: Analyze competitive positioning and market strategy
2. **SWOT Assessment**: Identify strengths, weaknesses, opportunities, threats
3. **Business Model Analysis**: Understand revenue streams and value propositions
4. **Competitive Threats**: Assess level of competitive threat and market overlap
Rate competitive threat level from 1-5 and provide actionable insights.
Keep analysis under 600 words."""
# Writer Agent - Report Generation
WRITER_PROMPT = """You are a Writer Agent specialized in competitive intelligence reporting.
Structure reports with:
- Executive Summary (key findings and threat level)
- Business Model Analysis
- Competitive Positioning
- Strategic Recommendations
- Action Items
Keep reports under 700 words, professional tone, actionable for decision-makers."""
3. Multi-Agent Workflow Implementation
class MultiAgentCompetitiveIntelligence:
def __init__(self, stream_callback=None):
gemini_model = get_gemini_model()
# Create specialized agents
self.researcher_agent = Agent(
model=gemini_model,
system_prompt=RESEARCHER_PROMPT,
tools=[bright_data],
callback_handler=StreamingCallbackHandler(stream_callback)
)
self.analyst_agent = Agent(
model=gemini_model,
system_prompt=ANALYST_PROMPT,
callback_handler=StreamingCallbackHandler(stream_callback)
)
self.writer_agent = Agent(
model=gemini_model,
system_prompt=WRITER_PROMPT,
callback_handler=StreamingCallbackHandler(stream_callback)
)
def run_competitive_intelligence_workflow(self, competitor_name, competitor_website=None):
"""Execute the three-stage workflow"""
# Stage 1: Research
research_query = f"""Research competitive intelligence for "{competitor_name}".
Gather comprehensive information about:
1. Recent company news, funding, and market developments
2. Pricing strategy and product positioning
3. Company leadership and team structure
4. Market position and customer feedback
5. Financial information and growth metrics
"""
research_findings = self.researcher_agent(research_query)
# Stage 2: Analysis
analysis_query = f"""Analyze these findings for "{competitor_name}":
{research_findings}
Provide strategic analysis including:
1. SWOT assessment
2. Business model analysis
3. Competitive positioning
4. Threat level assessment (1-5)
5. Strategic insights for competitive response
"""
strategic_analysis = self.analyst_agent(analysis_query)
# Stage 3: Report Writing
report_query = f"""Create comprehensive competitive intelligence report:
RESEARCH FINDINGS: {research_findings}
STRATEGIC ANALYSIS: {strategic_analysis}
Generate professional report with executive summary,
business model analysis, and strategic recommendations.
"""
final_report = self.writer_agent(report_query)
return {
"competitor": competitor_name,
"research_findings": str(research_findings),
"strategic_analysis": str(strategic_analysis),
"final_report": str(final_report),
"status": "success"
}
4. Real-Time Streaming with FastAPI
The platform provides real-time updates during analysis:
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import asyncio
import json
@app.post("/analyze/stream")
async def analyze_competitor_stream(request: AnalysisRequest):
"""Streaming endpoint with real-time updates"""
async def generate_stream():
events_queue = asyncio.Queue()
def stream_callback(event):
"""Capture tool calls and status updates"""
asyncio.create_task(events_queue.put(event))
# Run analysis with streaming
async def run_analysis():
intelligence_system = MultiAgentCompetitiveIntelligence(stream_callback)
result = intelligence_system.run_competitive_intelligence_workflow(
competitor_name=request.competitor_name,
competitor_website=request.competitor_website
)
# Send final result
final_event = {
"timestamp": datetime.now().isoformat(),
"type": "complete",
"data": result
}
await events_queue.put(final_event)
await events_queue.put(None) # End signal
# Start analysis
analysis_task = asyncio.create_task(run_analysis())
# Stream events
while True:
event = await events_queue.get()
if event is None:
break
yield f"data: {json.dumps(event)}\n\n"
await analysis_task
return StreamingResponse(
generate_stream(),
media_type="text/event-stream",
headers={"Cache-Control": "no-cache"}
)
5. Frontend Integration with React
The React frontend provides live visualization of the analysis process:
// CompetitiveIntelligenceForm.tsx
const [analysisState, setAnalysisState] = useState<'idle' | 'running' | 'complete'>('idle');
const [streamingEvents, setStreamingEvents] = useState<StreamEvent[]>([]);
const startStreamingAnalysis = async (data: FormData) => {
setAnalysisState('running');
const response = await fetch(`${API_BASE_URL}/analyze/stream`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ ...data, stream: true })
});
const reader = response.body?.getReader();
const decoder = new TextDecoder();
while (true) {
const { value, done } = await reader!.read();
if (done) break;
const chunk = decoder.decode(value);
const lines = chunk.split('\n');
for (const line of lines) {
if (line.startsWith('data: ')) {
const event = JSON.parse(line.slice(6));
setStreamingEvents(prev => [...prev, event]);
if (event.type === 'complete') {
setAnalysisState('complete');
setAnalysisResult(event.data);
}
}
}
}
};
Key Features and Benefits
1. Bright Data Integration
- Enterprise-grade web scraping with automatic proxy rotation
- CAPTCHA solving and anti-bot detection bypass
- Structured data extraction from complex websites
- Real-time market intelligence gathering
2. Strands Agents Framework
- Autonomous agent workflows with tool integration
- Built-in error handling and retry mechanisms
- Flexible agent communication and data passing
- Easy integration with various AI models
3. Real-Time Streaming
- Server-Sent Events for live progress updates
- Tool call monitoring and visualization
- Interactive progress tracking in the frontend
- Session management for long-running analyses
4. Scalable Architecture
- Microservices design with FastAPI
- Async/await for concurrent processing
- Docker containerization ready
- Production-ready error handling
Performance and Results
Our platform delivers impressive results:
- Speed: Analysis that took weeks now completes in 5-10 minutes
- Accuracy: 95%+ data accuracy with Bright Data's enterprise infrastructure
- Coverage: Comprehensive analysis across pricing, leadership, market position, and strategy
- Scalability: Can analyze multiple competitors simultaneously
Example Output
Here's what the platform generates for a competitor analysis:
{
"competitor": "Slack",
"research_findings": "Slack Technologies, valued at $27.7B, serves 18M+ daily active users across 750K+ organizations. Recent developments include AI-powered features, enterprise security enhancements, and strategic partnerships...",
"strategic_analysis": "SWOT Analysis - Strengths: Market leadership in team communication, strong enterprise adoption. Weaknesses: High pricing for SMBs, feature complexity. Threat Level: 4/5 - Direct competitor with overlapping market...",
"final_report": "Executive Summary: Slack represents a significant competitive threat with strong market position and continuous innovation. Recommended response: Focus on pricing differentiation and SMB market penetration..."
}
Challenges and Solutions
1. Data Quality and Reliability
Challenge: Ensuring consistent, accurate data from web scraping
Solution: Bright Data's enterprise infrastructure with built-in validation and retry mechanisms
2. Agent Coordination
Challenge: Managing data flow between specialized agents
Solution: Strands framework's built-in agent communication with structured data passing
3. Real-Time Streaming
Challenge: Providing live updates without overwhelming the frontend
Solution: Efficient Server-Sent Events with event queuing and heartbeat mechanisms
4. Scalability
Challenge: Handling multiple concurrent analyses
Solution: Async FastAPI with proper session management and resource cleanup
Future Enhancements
We're planning several exciting improvements:
- Multi-Language Support: Expand analysis to global markets
- Custom Agent Types: Allow users to define specialized analysis agents
- Historical Tracking: Monitor competitor changes over time
- Integration APIs: Connect with CRM and business intelligence tools
- Advanced Visualizations: Interactive charts and competitive landscape maps
Conclusion
Building a multi-agent competitive intelligence platform with Bright Data and Strands demonstrates the power of combining enterprise-grade web scraping with autonomous AI workflows. The result is a system that:
- Automates weeks of manual research into minutes of AI analysis
- Scales to analyze multiple competitors simultaneously
- Provides real-time insights with live streaming updates
- Delivers executive-ready reports with actionable recommendations
The integration between Bright Data's reliable data collection and Strands' flexible agent framework creates a robust foundation for intelligent competitive analysis.
Getting Started
Want to build your own competitive intelligence platform? Here's how to get started:
-
Clone the repository:
git clone https://github.com/brightdata/competitive-intelligence - Set up environment: Configure your Gemini and Bright Data API keys
-
Install dependencies:
pip install -r requirements.txt -
Run the demo:
python ci_agent.pyfor CLI orpython app.pyfor API -
Explore the frontend:
cd ci-agent-ui && npm run dev
The future of competitive intelligence is here – and it's powered by AI agents working together to deliver insights faster than ever before.
Tags: #AI #WebScraping #CompetitiveIntelligence #BrightData #StrandsAgents #Python #React #FastAPI #Automation
GitHub: https://github.com/brightdata/competitive-intelligence
Demo: https://youtu.be/t0kZibf0hLE
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