The Problem
On Monday you tested the 3 prompts in ChatGPT. You saw how extraction → validation → analysis works. But here's reality: you can't ask your strategy team to manually research 50 competitors every week. One analyst spending 3 hours per day running prompts? That's $90/day in labor costs. Multiply that across a growing startup and you're looking at $27,000/year just on competitive research. Plus the inconsistency that leads to missed market shifts and strategic blind spots.
See It Work
Watch the 3 prompts chain together automatically. This is what you'll build.
Watch It Work
See the AI automation in action
The Code
Three levels: start simple, add reliability, then scale to production. Pick where you are.
When to Level Up
Simple API Calls
- Sequential API calls (extract → validate → analyze)
- Basic error handling with try/catch
- Manual trigger (run script when needed)
- Save reports locally as JSON files
- ~$20-30/month in API costs
With Retries & Logging
- Exponential backoff retries (3 attempts)
- Structured logging (Winston/Python logging)
- Timeout protection (60s per request)
- Automatic report generation and storage
- Summary reports across competitors
- ~$100-150/month in API costs
Production with LangGraph
- Workflow orchestration with state management
- Automatic data enrichment from multiple sources
- Quality gates and validation checkpoints
- Concurrent batch processing (5-10 at once)
- Retry logic with conditional branching
- Automated web scraping integration
- ~$300-500/month in API + infrastructure costs
Multi-Agent System
- Specialized agents (scraping, analysis, monitoring, alerting)
- Real-time competitor monitoring and alerts
- Automated data collection from 20+ sources
- Trend analysis and predictive insights
- Integration with CRM and BI tools
- Custom dashboards and reporting
- Load balancing and queue management
- ~$1000-2000/month in API + infrastructure costs
Tech/Strategy Gotchas
5 things that will bite you if you're not careful
Rate Limiting from Web Scraping
Use rotating proxies and respect robots.txt
# Respectful web scraping with rate limiting
import requests
from time import sleep
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
class RespectfulScraper:
def __init__(self, delay_seconds=2):Stale Data Leading to Wrong Decisions
Implement data freshness tracking and automated updates
# Data freshness tracking
from datetime import datetime, timedelta
from typing import Dict, Optional
class FreshnessTracker:
def __init__(self, max_age_days: int = 30):
self.max_age = timedelta(days=max_age_days)
self.data_cache: Dict[str, Dict] = {}Inconsistent Competitor Naming
Implement entity resolution and canonical naming
# Entity resolution for competitor names
from difflib import SequenceMatcher
from typing import Dict, List, Optional
class CompetitorResolver:
def __init__(self):
self.canonical_names: Dict[str, str] = {}
self.aliases: Dict[str, List[str]] = {}Missing Context in Automated Analysis
Inject company context into analysis prompts
# Context-aware competitive analysis
from typing import Dict, List
import json
class ContextualAnalyzer:
def __init__(self, company_context: Dict):
self.context = company_context
Analysis Paralysis from Too Much Data
Create tiered reports: executive summary, tactical details, raw data
# Tiered reporting system
from typing import Dict, List
from enum import Enum
class ReportLevel(Enum):
EXECUTIVE = "executive" # 1-page summary
TACTICAL = "tactical" # 5-page actionable insights
DETAILED = "detailed" # Full analysis with dataAdjust Your Numbers
❌ Manual Process
✅ AI-Automated
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