ADS Solutions

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ADS Solutions: Automated E-commerce Bridge

Role: Lead Developer | Duration: 2024

ADS Solutions is a suite of sophisticated software tools designed to bridge the gap between Amazon and eBay marketplaces. The system leverages Amazon Prime's 5% cash back benefit while providing international customers with a simplified purchasing experience, eliminating the need for complex customs paperwork.

The solution comprises multiple integrated components:

  • Order Management: Automated processing of eBay orders with direct fulfillment through Amazon
  • Price Optimization: Dynamic pricing system accounting for cash back benefits and market conditions
  • International Bridge: Streamlined process for international customers to purchase through domestic channels
  • Automation Suite: Complete end-to-end automation of order processing and fulfillment

Automation Implementation Notice: This project utilizes sophisticated proxy rotation and browser fingerprinting techniques to mitigate bot detection systems. While these methods were implemented for educational purposes and proof of concept, users should be aware that:

  • Automated interactions with e-commerce platforms may violate terms of service
  • Proxy rotation and browser fingerprinting are used to demonstrate technical capabilities
  • Implementation details are shared for educational purposes only
  • Users should review and comply with platform policies before deployment

Bot Detection Mitigation Techniques

  • Proxy Rotation: Dynamic IP switching using residential proxies
  • Browser Fingerprinting: Randomized device signatures and user agents
  • Request Patterns: Human-like timing and behavior patterns
  • Session Management: Cookie and session handling for authenticity
Python Selenium eBay API Amazon API WebDriver Data Processing Error Handling

Problem & Solution

International Shipping Challenges

Many international customers face significant barriers when trying to purchase from US-based Amazon:

  • Complex customs documentation requirements
  • Limited shipping options to certain countries
  • High international shipping costs
  • Inability to use Amazon Prime benefits

Our Solution

ADS Solutions bridges this gap by:

  • Providing a domestic shipping address for international orders
  • Automating customs documentation
  • Leveraging Prime shipping for faster delivery
  • Passing on Prime cashback benefits to reduce costs

Faster Delivery

2-day Prime shipping to our processing center, followed by expedited international shipping

Cost Savings

5% Prime cashback and optimized shipping routes reduce total costs

Simplified Process

Automated customs handling and documentation

System Architecture

graph TB subgraph Input EB[eBay Orders] PR[Price Monitor] end subgraph Core OP[Order Processor] PM[Price Manager] AM[Automation Module] end subgraph Output AZ[Amazon Orders] RP[Reports & Analytics] end EB --> OP PR --> PM OP --> AM PM --> AM AM --> AZ AM --> RP

Key Features

Automated Order Processing

  • Real-time order monitoring
  • Automated fulfillment
  • Status tracking & updates

International Support

  • Simplified customs process
  • Global shipping management
  • Currency conversion

Price Optimization

  • Cash back benefit integration
  • Dynamic pricing algorithms
  • Market rate monitoring

Security & Compliance

  • Secure data handling
  • Platform policy compliance
  • Transaction verification

Process Flow

sequenceDiagram participant EB as eBay participant ADS as ADS Solutions participant AZ as Amazon EB->>ADS: New Order Received ADS->>ADS: Validate Order ADS->>ADS: Price Check ADS->>AZ: Place Order AZ->>ADS: Order Confirmation ADS->>EB: Update Tracking ADS->>ADS: Log Transaction

Performance Metrics

Success Rate

99.8%

Order completion rate

Processing Time

< 2 min

Average order processing

Cost Savings

5%

Through cash back benefits

VerO Protection System

The Verified Rights Owner (VerO) Scanner is a crucial component that ensures compliance with intellectual property rights and brand protection policies across international markets. This system helps prevent potential issues with restricted or protected items while facilitating safe cross-border commerce.

International Shipping Compliance

  • Automated verification of export restrictions and regulations
  • Country-specific product eligibility checks
  • Customs classification and documentation preparation
  • Restricted item screening across multiple jurisdictions

Global Compliance

  • Multi-jurisdiction verification
  • Export control screening
  • Customs documentation

Risk Management

  • Proactive violation prevention
  • Automated listing removal
  • Risk score calculation

Legal Compliance

  • IP rights verification
  • Brand protection checks
  • Regulatory adherence

International Compliance Implementation


class InternationalCompliance:
    def __init__(self):
        self.country_rules = CountryRulesDatabase()
        self.customs_classifier = CustomsClassifier()
        self.export_validator = ExportValidator()

    async def verify_international_eligibility(self, product_data, destination_country):
        try:
            # Check export restrictions
            export_status = await self.export_validator.check_restrictions(
                product=product_data,
                destination=destination_country
            )
            
            # Classify for customs
            customs_info = await self.customs_classifier.get_classification(
                product_name=product_data.get('title'),
                category=product_data.get('category'),
                price=product_data.get('price'),
                origin_country='US'
            )
            
            # Verify country-specific rules
            country_compliance = await self.country_rules.verify_compliance(
                product_data=product_data,
                customs_info=customs_info,
                destination=destination_country
            )

            return {
                'is_eligible': all([
                    export_status.is_allowed,
                    country_compliance.is_compliant
                ]),
                'customs_code': customs_info.hs_code,
                'required_documents': customs_info.required_docs,
                'restrictions': export_status.restrictions,
                'compliance_notes': country_compliance.notes
            }
        except Exception as e:
            logger.error(f"International compliance check failed: {str(e)}")
            return None

Implementation Details

System Components

Initial Scraper

The scraper module is designed to efficiently collect product data while respecting rate limits and handling errors:

  • Asynchronous HTTP requests for improved performance
  • Proxy rotation to prevent IP blocks
  • Robust error handling and retry mechanisms
  • Smart parsing of product details and pricing

Auto Order System

Order Processing

The automated order system handles the entire fulfillment process:

  • Queue-based order processing for reliability
  • Real-time inventory and price verification
  • Automated error recovery and retry logic
  • Transaction logging and status updates

Data Cleaner

Data Processing

The data cleaning module ensures consistent and accurate product information:

  • Title standardization and blacklist filtering
  • Price normalization across currencies
  • Condition standardization
  • Shipping information parsing

Amazon Automation

Marketplace Integration

The Amazon automation module handles all marketplace interactions:

  • Rate-limited API requests
  • Smart product search and filtering
  • Prime eligibility verification
  • Order placement and tracking

Product Extractor

Information Extraction

The product information extractor uses NLP to process product details:

  • Brand and model extraction
  • Specification parsing
  • Feature identification
  • Category classification

Technical Challenges & Solutions

Rate Limiting

Managing API rate limits across multiple marketplaces while maintaining system responsiveness.

Solution:


class RateLimiter:
    def __init__(self, max_requests, time_window):
        self.max_requests = max_requests
        self.time_window = time_window
        self.requests = []
        self._lock = asyncio.Lock()

    async def acquire(self):
        async with self._lock:
            now = time.time()
            # Remove expired timestamps
            self.requests = [req for req in self.requests 
                           if now - req < self.time_window]
            
            if len(self.requests) >= self.max_requests:
                sleep_time = self.requests[0] + self.time_window - now
                if sleep_time > 0:
                    await asyncio.sleep(sleep_time)
            
            self.requests.append(now)

Data Synchronization

Maintaining consistency between marketplace inventories and order status.

Solution:


class InventorySync:
    def __init__(self):
        self.cache = Cache()
        self.lock_manager = LockManager()

    async def update_inventory(self, product_id, quantity):
        async with self.lock_manager.acquire(f"inventory:{product_id}"):
            try:
                # Update local cache
                await self.cache.set(f"inventory:{product_id}", quantity)
                
                # Sync with marketplaces
                await asyncio.gather(
                    self.update_amazon_inventory(product_id, quantity),
                    self.update_ebay_inventory(product_id, quantity)
                )
            except Exception as e:
                # Rollback on failure
                await self.rollback_inventory_update(product_id)
                raise

Component Downloads

Access the individual components of ADS Solutions. Each script is documented and includes setup instructions.

Initial Scraper

Product data collection and analysis tool optimized for MacBook Pro.

Python 397 lines
Download Script

Auto Order System

Automated order processing and fulfillment system with error handling.

Python 343 lines
Download Script

Data Cleaner

Data preprocessing and validation tool for MacBook Pro compatibility.

Python 283 lines
Download Script

Amazon Automation

Core Amazon marketplace interaction and order processing module.

Python 422 lines
Download Script

Product Info Extractor

Detailed product information extraction and processing system.

Python 428 lines
Download Script

Code Implementation

Initial Scraper Implementation


class ProductScraper:
    def __init__(self):
        self.session = aiohttp.ClientSession()
        self.proxy_manager = ProxyManager()
        self.rate_limiter = RateLimiter(max_requests=10, window=60)

    async def scrape_products(self, search_terms):
        results = []
        for term in search_terms:
            proxy = await self.proxy_manager.get_next_proxy()
            await self.rate_limiter.acquire()
            
            try:
                async with self.session.get(
                    f"{BASE_URL}/search",
                    params={"q": term},
                    proxy=proxy,
                    headers=self.get_headers()
                ) as response:
                    data = await response.json()
                    results.extend(self.parse_products(data))
            except Exception as e:
                logger.error(f"Error scraping {term}: {str(e)}")
                continue
        
        return results

    def parse_products(self, data):
        products = []
        for item in data["items"]:
            product = {
                "id": item["id"],
                "title": self.clean_title(item["title"]),
                "price": self.extract_price(item["price"]),
                "condition": item.get("condition", "New"),
                "shipping": self.parse_shipping(item)
            }
            products.append(product)
        return products

Key Implementation Details

  • Asynchronous HTTP requests for improved performance
  • Integrated proxy rotation system
  • Rate limiting to prevent API throttling
  • Robust error handling and retry logic
  • Smart parsing of product details and pricing

Automated Order System


class OrderProcessor:
    def __init__(self):
        self.order_queue = asyncio.Queue()
        self.session_manager = SessionManager()
        self.inventory = InventoryTracker()

    async def process_orders(self):
        while True:
            order = await self.order_queue.get()
            session = await self.session_manager.get_session()
            
            try:
                # Verify inventory and price
                if not await self.inventory.check_availability(order.product_id):
                    raise OutOfStockError(order.product_id)
                
                # Process payment
                payment_result = await self.process_payment(order)
                if not payment_result.success:
                    raise PaymentError(payment_result.error)
                
                # Place order
                order_result = await self.place_order(
                    session=session,
                    product_id=order.product_id,
                    quantity=order.quantity,
                    shipping=order.shipping_details
                )
                
                await self.update_order_status(order.id, order_result)
                
            except Exception as e:
                await self.handle_order_error(order, e)
            finally:
                self.order_queue.task_done()
                await self.session_manager.release_session(session)

System Architecture

  • Queue-based order processing system
  • Session management for concurrent orders
  • Real-time inventory verification
  • Comprehensive error handling
  • Automated status updates

Data Cleaner Implementation


class DataCleaner:
    def __init__(self):
        self.blacklist = self.load_blacklist()
        self.brand_aliases = self.load_brand_aliases()
        self.condition_map = self.load_condition_mappings()

    def clean_product_data(self, raw_data):
        cleaned = {
            "title": self.clean_title(raw_data["title"]),
            "brand": self.normalize_brand(raw_data.get("brand")),
            "price": self.normalize_price(raw_data["price"]),
            "condition": self.standardize_condition(raw_data["condition"]),
            "shipping": self.parse_shipping_info(raw_data["shipping"])
        }
        
        if self.validate_product(cleaned):
            return cleaned
        return None

    def clean_title(self, title):
        # Remove blacklisted terms
        for term in self.blacklist:
            title = title.replace(term, "")
        
        # Standardize formatting
        title = re.sub(r'\s+', ' ', title)
        title = title.strip().title()
        
        return title

    def normalize_price(self, price_data):
        try:
            # Extract numeric value
            price = re.search(r'[\d,.]+', price_data).group()
            price = float(price.replace(',', ''))
            
            # Convert to USD if needed
            if 'currency' in price_data:
                price = self.convert_currency(
                    price,
                    price_data['currency'],
                    'USD'
                )
            
            return round(price, 2)
        except Exception:
            return None

Data Processing Features

  • Advanced text cleaning and normalization
  • Brand and condition standardization
  • Price normalization with currency conversion
  • Blacklist filtering for restricted terms
  • Comprehensive data validation

Amazon Automation System


class AmazonAutomation:
    def __init__(self):
        self.browser = await self.init_browser()
        self.cart_manager = CartManager()
        self.checkout = CheckoutAutomation()

    async def process_order(self, order_details):
        try:
            # Initialize new browser session
            await self.browser.new_context(
                proxy=await self.get_proxy(),
                viewport={'width': 1280, 'height': 800}
            )
            
            # Search and select product
            product_page = await self.search_product(
                order_details.product_id
            )
            
            if not product_page:
                raise ProductNotFoundError()
            
            # Add to cart with specified quantity
            cart_result = await self.cart_manager.add_to_cart(
                product_page,
                order_details.quantity
            )
            
            # Process checkout
            order_result = await self.checkout.process(
                shipping_address=order_details.shipping,
                payment_method=order_details.payment
            )
            
            return order_result
            
        except Exception as e:
            logger.error(f"Order processing failed: {str(e)}")
            raise
        finally:
            await self.browser.close()

Automation Features

  • Headless browser automation
  • Dynamic proxy rotation
  • Smart product search and selection
  • Automated cart management
  • Secure checkout process

Product Information Extractor


class ProductExtractor:
    def __init__(self):
        self.nlp = spacy.load("en_core_web_sm")
        self.brand_patterns = self.load_brand_patterns()
        self.spec_patterns = self.load_spec_patterns()

    async def extract_product_info(self, html_content):
        soup = BeautifulSoup(html_content, 'html.parser')
        
        # Extract basic information
        title = self.extract_title(soup)
        brand = self.extract_brand(title)
        specs = self.extract_specifications(soup)
        
        # Process description
        description = soup.find(class_="product-description")
        if description:
            doc = self.nlp(description.text)
            
            # Extract key features
            features = self.extract_features(doc)
            
            # Extract measurements
            measurements = self.extract_measurements(doc)
            
            # Identify compatibility
            compatibility = self.extract_compatibility(doc)
        
        return {
            "title": title,
            "brand": brand,
            "specifications": specs,
            "features": features,
            "measurements": measurements,
            "compatibility": compatibility
        }

    def extract_features(self, doc):
        features = []
        for sent in doc.sents:
            if self.is_feature_sentence(sent):
                features.append(self.clean_feature(sent.text))
        return features

NLP Processing Features

  • Natural language processing for feature extraction
  • Pattern matching for specifications
  • Brand and model identification
  • Compatibility analysis
  • Measurement extraction and standardization