Asad marcus for kirodotdev

Posted on Aug 6

Building EVI : An AI-Powered Economic Vitality Analysis Platform with Kiro

#kiro #programming #ai #aws

How I leveraged Kiro's autonomous development capabilities to build a comprehensive economic analysis EVI tool that processes real-world data from multiple countries and generates actionable business insights

The Challenge: Making Economic Data Accessible

As a developer who recently joined the Kiro organization, I wanted to showcase how Kiro's AI-powered development environment can accelerate complex project development. I decided to build EVI (Economic Vitality Index) - a platform that analyzes the economic health of any location worldwide using real-time data and AI insights.

The challenge was ambitious: create a system that could:

Process economic data from multiple countries (EU, USA, UK, Pakistan)
Integrate with Google Maps and Places APIs for real-time business data
Generate comprehensive economic vitality scores (0-100 scale)
Provide actionable insights for businesses and investors
Handle complex data analysis and visualization
Support global coverage with fallback mechanisms
Implement secure API usage to avoid expensive charges

The Reality Check: Building this manually would have required weeks of:

Setting up Flask blueprints and database schemas
Implementing complex data processing pipelines
Managing multiple API integrations with proper error handling
Creating authentication and session management
Building data visualization and export features

Enter Kiro: My AI Development Partner

Working with Kiro transformed how I approached this complex project. Instead of spending weeks setting up boilerplate code and debugging API integrations, Kiro helped me focus on the core business logic while handling the heavy lifting.

What Kiro Brought to the Table

Autonomous Code Generation: Kiro generated complete Flask blueprints with 15+ API endpoints, comprehensive database models, and complex data processing pipelines. What would have taken days of manual coding was completed in hours with proper error handling and validation built-in.

Intelligent Architecture Decisions: When I described needing to process economic data from multiple countries, Kiro automatically structured a modular service architecture with separate handlers for EU (Eurostat), USA (Census/BEA), UK (ONS), and Pakistan (PBS) data sources.

Smart Error Handling: Kiro implemented sophisticated fallback mechanisms I hadn't considered - when Google Places API fails, it generates realistic synthetic data based on location demographics. When external APIs hit rate limits, it gracefully degrades to cached data.

Database Design: Kiro designed a comprehensive SQLAlchemy schema with 8 interconnected models for analysis history, user sessions, invite codes, and cached results - including proper indexing and relationship management.

The EVI Architecture

Here's what we built together:

Core Components

# EVI Analysis Engine - Generated with Kiro's help
@dataclass
class LocationMetrics:
    latitude: float
    longitude: float
    radius_meters: int
    business_count: int
    business_density: float
    average_rating: float
    total_reviews: int
    business_type_distribution: Dict[str, int]
    foot_traffic_index: float
    economic_activity_score: float
    infrastructure_quality: float
    accessibility_score: float
    competition_index: float
    growth_trend: float
    last_updated: datetime

@dataclass
class BusinessData:
    id: str
    name: str
    business_type: str
    latitude: float
    longitude: float
    rating: float
    review_count: int
    price_level: int
    address: str
    foot_traffic_score: float
    digital_presence_score: float
    last_updated: datetime

Multi-Source Data Integration

EVI pulls data from multiple sources with intelligent processing:

class EnhancedDataService:
    def __init__(self):
        self.eu_data = {}      # Eurostat regional data
        self.usa_data = {}     # Census & BEA data
        self.pakistan_data = {} # Pakistan Bureau of Statistics
        self.uk_data = {}      # ONS (Office for National Statistics)
        self.load_all_data()

    def get_enhanced_analysis(self, city_name: str, country_code: str):
        """Combine multiple data sources for comprehensive analysis"""
        # GDP per capita, unemployment rates, business demographics
        regional_data = self.get_regional_economic_data(city_name, country_code)
        # Population, age distribution, education levels
        demographic_data = self.get_demographic_data(city_name, country_code)
        # Business formation rates, industry composition
        business_data = self.get_business_ecosystem_data(city_name, country_code)

Data Sources Include:

Google Places API: Real-time business data, ratings, and foot traffic estimates
World Bank API: Economic indicators, development metrics, and country comparisons
Eurostat: EU regional GDP, unemployment rates, and business demographics
National Statistics: Country-specific datasets (Census, ONS, PBS)
Enhanced Processing: PDF parsing, Excel integration, and data normalization

Smart Analysis Pipeline

@evi_bp.route('/analyze', methods=['POST'])
def analyze_city():
    """Analyze a city's economic vitality with comprehensive data integration"""
    try:
        data = request.get_json()
        city_name = data.get('city')
        radius = data.get('radius', 500)

        # Multi-step analysis process
        analysis = evi_engine.calculate_evi_analysis(city_name, radius)

        # Enhanced with real economic data
        enhanced_data = enhanced_data_service.get_enhanced_analysis(
            city_name, 
            analysis.country_code
        )

        # Auto-save comprehensive history
        history_record = AnalysisHistory(
            city_name=city_name,
            evi_score=analysis.evi_score,
            business_vitality=analysis.business_vitality,
            economic_stability=analysis.economic_stability,
            growth_potential=analysis.growth_potential,
            risk_level=analysis.risk_level.value,
            investment_grade=analysis.investment_grade,
            business_density=analysis.location_metrics.business_density,
            foot_traffic_index=analysis.location_metrics.foot_traffic_index,
            competition_index=analysis.location_metrics.competition_index,
            # ESG scoring integration
            esg_overall=enhanced_data.get('esg_score', {}).get('overall', 0),
            confidence_level=analysis.confidence_level
        )

        return jsonify({'success': True, 'data': result})
    except Exception as e:
        return jsonify({'error': str(e)}), 500

Key Features We Built

🎯 Economic Vitality Scoring Algorithm

EVI generates a comprehensive 0-100 score by analyzing:

Business Vitality (25%): Density, diversity, and health of local businesses
Economic Stability (25%): Employment rates, income levels, economic resilience
Growth Potential (25%): Demographic trends, infrastructure development, investment flow
Risk Assessment (25%): Market volatility, regulatory environment, competition intensity

def calculate_evi_score(self, location_metrics, enhanced_data):
    """Calculate comprehensive EVI score with weighted components"""
    business_vitality = self._calculate_business_vitality(location_metrics)
    economic_stability = self._calculate_economic_stability(enhanced_data)
    growth_potential = self._calculate_growth_potential(enhanced_data)
    risk_factors = self._calculate_risk_factors(location_metrics, enhanced_data)

    evi_score = (
        business_vitality * 0.25 +
        economic_stability * 0.25 +
        growth_potential * 0.25 +
        (100 - risk_factors) * 0.25
    )

    return min(100, max(0, evi_score))

📊 Multi-Dimensional Analysis Engine

Business Ecosystem Mapping: 12+ business categories with density analysis
Demographic Integration: Age distribution, education levels, income brackets
Infrastructure Assessment: Transportation, utilities, digital connectivity
Competition Analysis: Market saturation, pricing trends, opportunity gaps
ESG Scoring: Environmental, Social, and Governance factors

🌍 Global Coverage with Smart Localization

200+ Major Cities: Pre-configured with local economic data
Fallback Generation: AI-powered synthetic data when APIs are unavailable
Multi-Language Support: Handles international city names and addresses
Currency Normalization: Converts all economic indicators to USD equivalents

📈 Advanced Analytics & Reporting

Trend Analysis: 12-month historical tracking with seasonal adjustments
Comparative Analysis: Side-by-side city comparisons with benchmarking
Export Capabilities: PDF reports, Excel dashboards, API data feeds
Real-time Updates: Live data refresh with change notifications

🔐 Enterprise-Grade Features

Kiro helped implement production-ready features:

Authentication System: Multi-tier access (admin, invite-based, public)
Rate Limiting: API usage controls to prevent abuse
Caching Layer: Redis integration for performance optimization
Email Integration: Automated reports and notifications
Admin Dashboard: Real-time system monitoring and user management

The Kiro Advantage in Action

Before Kiro:

Manual API integration and error handling
Hours spent on boilerplate Flask setup
Complex data processing logic written from scratch
Debugging authentication and session management

With Kiro:

Automated blueprint generation with proper error handling
Intelligent fallback mechanisms for API failures
Pre-built authentication and session management
Smart data processing pipelines

Real Example: API Integration

When I needed to integrate the Google Places API safely (avoiding expensive charges), I simply described my requirements to Kiro:

"I need to fetch business data using Google Places API but only use free fields to avoid charges"

Kiro generated:

def fetch_real_business_data(self, lat: float, lng: float, radius: int = 500):
    """
    Fetch real business data using Google Places API with STRICT controls 
    to avoid expensive SKUs. ONLY uses the most basic free fields.
    """
    try:
        places_result = gmaps.places_nearby(
            location=(lat, lng),
            radius=radius,
            type='establishment'
        )

        # Process only free fields to avoid charges
        for place in places_result.get('results', []):
            # Only access confirmed free fields
            geometry = place.get('geometry', {})
            name = place.get('name', f'Business {i+1}')
            rating = place.get('rating', 0.0)
            # ... safe field processing

Performance & Scalability

EVI handles:

Real-time analysis of any global location
Multi-source data integration from 4+ different APIs
Intelligent caching to optimize API usage
Fallback mechanisms when external services are unavailable

What's Next?

The EVI platform demonstrates how Kiro can accelerate complex, data-driven application development. Future enhancements include:

Machine learning models for predictive analysis
Enhanced ESG (Environmental, Social, Governance) scoring
Real-time market sentiment analysis
Mobile app development

Try It Yourself

The beauty of working with Kiro is that complex projects become manageable. Whether you're building:

Data analysis platforms
API-heavy applications
Multi-service integrations
Complex business logic

Kiro's autonomous development capabilities can help you focus on what matters: solving real problems with code.

Want to see EVI in action? The platform analyzes economic vitality for any location worldwide, providing insights that help businesses make informed decisions about expansion, investment, and market entry.

Interested in Kiro? Join the organization and experience AI-powered development that actually accelerates your workflow instead of getting in the way.

What complex project would you build with an AI development partner? Share your ideas in the comments!

NOTE : This blog is made with Kiro IDE.