Overview

StampAuctionNetwork.com, a $400M/year online stamp auction platform, partnered with Bacancy to modernize their legacy MyStampStore system by embedding AI-driven search and intelligent recommendations. Our goal was to enhance user experience with natural language-based search, personalized bidding suggestions, and intelligent lot discovery while preserving the existing SQL Server and MyStampStore infrastructure.

Technical Stack

  • SQL Server
  • Python
  • OpenAI API
  • HuggingFace Transformers
  • FastAPI
  • HTML/CSS
  • Flask
  • spaCy
  • scikit-learn

    • Industry

    Collectibles / Auction Technology

  • region

    • Region

    United States

  • project-size

    • Project Size

    Non- Disclosable

Highlights

Integrated AI into the legacy system without disrupting platform operations

Enabled natural language search using custom-trained NLP models

Built personalized recommendations based on user behavior and item metadata

Laid the foundation for future vision-based item search (from images)

Challenges & Solutions

Integrating AI into MyStampStore Environment

  • Solution: Our AI Experts built a lightweight Python-based AI engine and exposed its functionality via RESTful endpoints using FastAPI. These endpoints were seamlessly consumed bythe components of MyStampStore’platfom, enabling modern features without disrupting existing infrastructure.

Handling Large and Varied Data Sources

  • Solution: Our data engineers cleaned, normalized, and structured bidding history, lot descriptions, and metadata. We used embedding techniques (OpenAI + HuggingFace) to train semantic search and recommendation models that performed consistently across varied inputs.

Building Personalized Search & Recommendations

  • Solution: We developed AI algorithms that analyzed user activity and preferences, returning relevant lots and bidding suggestions in real-time. These models continuously evolved using user behavior feedback, ensuring accuracy and personalization.

Preparing for Vision-Based Lot Search

  • Solution: We modularized the AI engine to support image embeddings from tools like Google Vision and OpenCV in future iterations, ensuring the architecture is scalable for visual search expansion.

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Core Features

  • Natural language-based search powered by AI
  • AI integration through secure APIs
  • Personalized item and bidding recommendations
  • Scalable ML pipeline for training and retraining
  • User activity tracking and behavioral pattern analysis
  • Modular architecture enabling future vision-based search
  • no.-of-resources

    • No. of Developers

    04

  • time-frame

    • Time Period

    3 Months

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