ImageTwin image-similarity analysis
Forensics · E-commerce · IP · Forensics / Visual Search / Legal Tech
Search time before versus after, on a representative image repository.
- Client
- Forensics · E-commerce · IP
- Industry
- Forensics / Visual Search / Legal Tech
- Scope
- Vector-based image-similarity search for forensic and IP use.
Inside the project
Challenge
Visual comparison across image databases was manual and slow. The aim was fast, AI-driven image matching.
Solution
ImageTwin lets forensic professionals identify similar images instantly using vector-based similarity matching — CLIP/ViT embeddings, preprocessing for occlusion/blur/low-resolution recovery, and high-speed nearest-neighbor search (FAISS, Annoy) exposed via UI and REST APIs.
Our approach
We embedded images with CLIP / Vision-Transformer models and served high-speed nearest-neighbor search (FAISS, Annoy) through a UI and REST APIs. The flow runs preprocessing → embedding generation → vector indexing → FAISS/Annoy retrieval → filtering → ranked review → API integration, so an engineer can query with a drawing, BIM render, component image or fabrication reference and get ranked matches with source, project, format and similarity context. That powers design reuse, duplicate-work reduction, prior-project discovery, component matching, and forensic / intellectual-property review — turning slow manual visual search into instant, defensible matching.