Anse

Provides a browser-based visual interface where users click on page elements to define extraction patterns without writing code. The system likely uses DOM inspection APIs and CSS selector generation to map user clicks to structural selectors, then converts these selections into reusable extraction rules that can be applied across multiple pages with similar DOM structures.

Handles JavaScript-rendered pages by executing page scripts in a headless browser environment before extraction, rather than parsing raw HTML. This allows extraction from single-page applications and dynamically-loaded content that would be invisible to simple HTTP-based scrapers. The system likely maintains a browser pool and manages page lifecycle (load, wait for selectors, extract) to handle async content loading.

Applies schema-based validation to extracted data, checking field types, required fields, format constraints, and value ranges before returning results. The system likely uses a declarative schema definition (JSON Schema or similar) that users configure through the UI, then validates each extracted record against this schema, optionally cleaning or rejecting invalid data based on configured rules.

Allows users to define extraction patterns once and apply them across multiple pages with similar structure, automatically handling pagination and URL pattern matching. The system likely uses template matching or structural similarity detection to identify pages that match a defined pattern, then applies the same extraction rules to each matched page, aggregating results into a single dataset.

Provides built-in transformations for extracted data such as text normalization, whitespace trimming, date parsing, unit conversion, and field mapping. The system likely exposes a library of transformation functions through the UI that users can chain together, applying them to extracted fields before output. Transformations may include regex-based text extraction, conditional field mapping, and aggregation operations.

Enables users to schedule recurring scraping jobs that run at specified intervals and optionally detect changes in extracted data compared to previous runs. The system likely maintains a job scheduler (cron-based or similar) and stores historical snapshots of extracted data, comparing new extractions against previous versions to identify additions, deletions, or modifications. Change detection may trigger notifications or webhooks.

Supports exporting extracted data to multiple formats and external systems including CSV, JSON, databases, and cloud storage (S3, Google Cloud Storage). The system likely provides pre-built connectors for common destinations and may support webhook-based push to custom endpoints. Export may be triggered manually or automatically as part of scheduled jobs.

Manages HTTP requests through configurable proxy pools and rate limiting to avoid IP blocks and respect target site policies. The system likely maintains a pool of proxy servers and distributes requests across them, with configurable delays between requests and per-domain rate limits. Users may configure proxy rotation strategies and request headers to mimic browser behavior.

Tracks versions of extraction rules and allows users to update rules when target site structure changes, with optional rollback to previous versions. The system likely maintains a version history of each extraction rule and may provide alerts when extraction fails (indicating potential site structure changes). Users can test updated rules against sample pages before deploying to production.

Exposes extraction capabilities through a REST or GraphQL API, allowing developers to trigger scraping jobs, retrieve results, and manage rules programmatically. The system likely provides authentication via API keys and supports both synchronous (wait for results) and asynchronous (poll for results) request patterns. API responses include structured data and metadata about extraction success/failure.