AI-Powered Expert Search and Retrieval Platform

Problem

Users needed to move from broad questions to relevant expert matches without scanning large amounts of disconnected profile or document data manually.

Constraints

• Quality of retrieval results
• Need for explainable search behaviour
• Evolving AI tooling and cost considerations

Viniak's Role

Applied AI/ML engineering, vector-search workflow design, product implementation and technical delivery support.

Team Context

This was part of product or client delivery work, with responsibilities focused on the engineering, architecture and implementation areas described in this case study.

Discovery and Decisions

The work started from the product problem, data shape, user workflows and delivery constraints before choosing implementation details.

Architecture and Approach

The solution direction combined product search UX, backend retrieval logic and AI-assisted ranking or summarisation patterns while keeping deterministic product flows around the AI layer.

Technologies Used

• LLMs
• Vector search
• React
• Node.js
• Python
• MongoDB

Implementation Highlights

• Expert-search workflow design
• LLM-assisted retrieval patterns
• Frontend and backend integration

Published Outcome

Delivered expert-search and retrieval workflows as part of real product engineering work.

Lessons Learned

AI search features need evaluation, fallback behaviour and clear user expectations as much as they need model integration.