Sievex

Overview

A configurable web-data automation platform for crawling, extraction, workflow execution, and AI-assisted exploration.

The problem

Collecting web data is only one part of the workflow. Requests need validation, crawling needs controlled execution, and extracted information needs a useful way to be retrieved and explored.

The solution

Sievex connects configurable crawling jobs, extraction, and automation workflows with a contextual assistant that uses crawl results and conversation history for follow-up exploration.

Architecture & data flow

A modular Spring Boot backend organizes REST endpoints, secure access, request validation, crawler execution, data processing, and AI assistance. Collected data and conversation history provide context for the assistant.

The engineering challenge

Connecting controlled crawling jobs with secure, contextual AI assistance.

Key features

• Secure authentication and role-based access
• Validated requests and configurable crawling jobs
• Web crawling and structured data extraction
• Modular automation workflows
• Controlled background processing
• Retrieval of collected crawl data
• Contextual AI assistance and conversation history
• AI-assisted exploration of extracted information

Design decisions

• Separate request validation from job execution so API and crawling responsibilities can evolve independently.
• Organize crawling and automation as modules with explicit inputs and outputs.
• Bring crawl data and conversation history into the assistant context for useful follow-up questions.

Performance considerations

Areas to evaluate as the workload grows; no benchmark results are claimed.

• Bound concurrent jobs and apply timeouts so slow remote pages do not consume all worker capacity.
• Plan for retries, cancellation, and isolated failures when extending background execution.
• Limit retrieved context and conversation length to control model latency and token cost as usage grows.

Security boundaries

Spring Security, JWT, and roles organize access. Crawling requests need URL validation; deployments should also review private-network restrictions and redirects. Extracted text is untrusted input and should remain separate from assistant instructions.

A look at the product

What I learned

Automation is most useful when execution is predictable and results remain accessible. AI introduces a new boundary: retrieved content can inform an answer, but it should not control the application.