Multi-Modal AI Processing Platform
The Challenge
Manual processing of visual content, product data, and document categorization created significant bottlenecks. Teams spent hours manually reviewing images, classifying products, conducting research, and organizing data across multiple systems. The workflow was error-prone and couldn't scale.
Users
Operations team, content managers, business analysts, and logistics coordinators (~100+ users)
Workflow
Daily intake of product images, documents, and data requiring multi-step categorization, enrichment, and classification before entering core business systems.
Constraints
Multiple data sources, complex categorization logic across dynamic dimensions, need for high accuracy to prevent downstream errors, real-time processing requirements.
AI Capabilities Implemented
Image Analysis
Computer vision for image recognition and classification, quality assessment, and feature extraction from product photos.
Document Transcription
Image editing, background removal, and enhancement for consistent product presentation.
Additional AI Features
- Automated categorization across multiple taxonomy dimensions using vision and text analysis
- Autonomous web research to enrich product data with specifications and competitive information
- AI-generated product descriptions, metadata, and categorization tags
- Intelligent categorization across several dynamic dimensions with cross-validation
- API-based AI services orchestration with confidence scoring and exception flagging
Results
Technical Architecture
Frontend
React web application with real-time dashboard and admin tools
Backend
Node.js / Express API with async job processing
Database
PostgreSQL for structured data, vector database for embeddings
Storage
Cloud object storage for images and processed artifacts
Integrations
- • Multiple AI APIs (vision, text generation, web search)
- • Legacy ERP systems
- • Internal business databases