Sandri - Enterprise Knowledge Management AI
Led development of a comprehensive RAG-based AI system for a major logistics company, integrating multiple data sources to create an intelligent knowledge management platform.
The Challenge
The logistics company was drowning in unstructured information spread across hundreds of PDFs, FAQs, internal documents, and video transcripts. Employees spent hours searching for answers, and critical knowledge was often buried or duplicated across different sources.
They needed a centralized, intelligent system that could understand natural language queries and provide accurate, contextual answers from their vast knowledge base.
Technical Solution
I architected and implemented a state-of-the-art Retrieval-Augmented Generation (RAG) system that combined multiple AI technologies:
- Document Processing Pipeline: Automated ingestion of PDFs, text files, and video transcripts with intelligent chunking and metadata extraction
- Vector Database: Implemented embedding-based similarity search using state-of-the-art language models for semantic understanding
- LLM Integration: Connected to advanced language models with custom prompt engineering for domain-specific responses
- MLOps Pipeline: Built continuous integration for knowledge base updates and model retraining workflows
Implementation & Results
The project was delivered successfully within the 4-month timeline, exceeding client expectations:
- 85% reduction in time spent searching for information
- 95% accuracy rate in question answering with proper source attribution
- Seamless integration with existing company workflows and tools
- Scalable architecture supporting thousands of concurrent users