Project case study · Yash Tank

Sievex

Sievex brings controlled web crawling, data processing and AI-assisted exploration into a backend-focused automation platform. It connects collecting web data with asking follow-up questions about that data.

  • Java
  • Spring Boot
  • Spring Security
  • MySQL
  • REST APIs
  • Spring AI
  • LLM APIs

Workflow

  1. Authenticate users and authorize access with Spring Security, JWT and roles.
  2. Validate crawling requests before executing dynamic crawling and product-data extraction workflows.
  3. Run automation jobs and process the collected data.
  4. Use crawl results and conversation history to support contextual AI assistance.

Architecture and scope

A modular backend organizes secure access, crawling workflows, automation and data processing. AI assistance uses the collected data and conversation history as context for follow-up questions.

Engineering focus

The engineering focus is the connection between access control, request validation, web-data workflows and contextual assistance. This project brings together my interests in backend engineering, automation and Generative AI.