Flarif AI
Senior AI/Software Engineer
About the Project
Flarif AI is a personalized AI assistant platform that lets individuals and businesses build secure, custom-trained AI systems. Users upload their own documents — PDFs, notes, manuals — and the platform creates a private knowledge base they can query conversationally. Built with a Retrieval-Augmented Generation (RAG) architecture, it ensures responses are grounded in the user's actual data rather than generic model outputs.
Key Highlights
- Designed and implemented the full RAG pipeline: document ingestion, chunking, embedding, vector storage, and retrieval
- Built the conversational interface with streaming responses for real-time interaction
- Implemented multi-tenant architecture so each user's data remains isolated and private
- Integrated OpenAI API with custom prompt engineering for context-aware answers
- Handles multiple document formats (PDF, DOCX, TXT) with automatic processing
Technical Challenges
The biggest challenge was balancing response quality with latency. Large documents needed intelligent chunking strategies to ensure relevant context was retrieved without overwhelming the model's context window. I implemented a hybrid search approach combining semantic similarity with keyword matching to improve retrieval accuracy.