Oleksandr Korol

Engineering case study

Turning an AI search prototype into a fully functional web service

A client had demonstrated natural-language item search as a prototype. The next step was to turn that promising mechanism into a complete service that could deliver semantic search over client data to end users.

LogisticsAI search platform

This case study describes commercial work at a level suitable for public sharing. Client-identifying and confidential implementation details are intentionally omitted.

01

The challenge

A successful model demonstration is only one part of a usable search product. The delivery also needed an application layer, durable data handling, an interface for users, and an operational environment around the search workflow.

02

Engineering approach

  1. Worked alongside the client’s data-science department to preserve the value of the original search mechanism while productizing it.
  2. Built the service boundary with Django and Django REST Framework and paired it with a React interface for end-user workflows.
  3. Used a recorded stack that included Hugging Face, NumPy, Pandas, PostgreSQL, Redis, DigitalOcean, and Caddy.

03

What the product enabled

  • Natural-language search over client-provided data
  • A web interface around the semantic-search workflow
  • API-backed integration between application and data-science components
  • A deployable service rather than a standalone prototype

04

Delivered outcomes

  • Advanced the client’s prototype into a fully functional web service.
  • Created a delivery path for making semantic search available to end users.
  • Connected the client’s data-science work with a complete web application stack.

These outcomes summarize the available project record; no client metrics have been inferred.

05

Technology used

  • Django
  • DRF
  • React
  • Redux
  • Mantine UI
  • NumPy
  • Pandas
  • Hugging Face
  • Digital Ocean
  • Caddy
  • Redis
  • PostgreSQL

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