Kadoa

Kadoa uses machine learning to automatically detect and extract data patterns from web pages without requiring manual CSS selectors or XPath expressions. The system analyzes page structure, identifies repeating elements, and learns extraction rules by observing examples, enabling non-technical users to scrape complex websites by simply pointing to desired data elements.

Kadoa handles JavaScript-rendered content by executing page scripts in a headless browser environment before extraction, capturing dynamically loaded data that static HTML parsing would miss. The system manages browser lifecycle, waits for dynamic content to load, and extracts data from the rendered DOM state.

Kadoa enables users to transform extracted data through field mapping, type conversion, string manipulation, and custom logic without writing code. The system supports common transformations (date parsing, currency conversion, text normalization) and allows chaining multiple transformation steps to clean and standardize data.

Kadoa provides dashboards and alerts for monitoring scraping job execution, data quality metrics, and error rates. The system tracks job success/failure, data volume trends, and quality issues, sending notifications when jobs fail or data quality degrades below thresholds.

Kadoa enables users to define scraping jobs that run on schedules (hourly, daily, weekly) or trigger-based conditions, storing results in databases or data warehouses. The system manages job queuing, retry logic, and failure notifications without requiring users to build orchestration infrastructure.

Kadoa automatically detects pagination patterns (next buttons, page numbers, infinite scroll) and traverses multiple pages to collect complete datasets. The system learns pagination logic from examples and applies it across similar page structures, collecting data from hundreds or thousands of pages without manual configuration per page.

Kadoa validates extracted data against user-defined schemas, detecting missing fields, type mismatches, and anomalies before data reaches downstream systems. The system can enforce required fields, data types, format constraints, and custom validation rules, quarantining invalid records for review.

Kadoa manages proxy rotation and IP cycling to avoid detection and blocking by target websites. The system distributes requests across multiple IP addresses, manages proxy pools, handles proxy failures, and implements intelligent backoff strategies when sites detect scraping activity.

Kadoa exposes scraped data via REST APIs and can push results to external systems via webhooks, enabling real-time data integration with downstream applications. The system handles authentication, payload formatting, retry logic, and delivery confirmation without requiring users to build custom integration code.

Kadoa provides a visual interface where users click on page elements to define what data to extract, eliminating the need to write CSS selectors or XPath expressions. The system records user selections, learns patterns from examples, and generates extraction rules automatically without requiring technical knowledge.

Kadoa implements sophisticated error handling that distinguishes between transient failures (temporary network issues, rate limiting) and permanent failures (page not found, authentication required), applying appropriate retry strategies. The system uses exponential backoff to avoid overwhelming target servers and includes circuit breaker patterns to prevent cascading failures.

Kadoa manages HTTP headers, user agents, and browser fingerprints to mimic legitimate browser traffic and avoid detection by anti-bot systems. The system rotates user agents, manages cookies and sessions, and implements realistic browser behavior patterns to appear as normal user traffic rather than automated scraping.