RecallAI Vault Knowledge Engine
AI-Powered Personal Second Brain with Semantic Vector Search & Multi-Format Parsing
An intelligent personal knowledge base that ingests YouTube videos, PDF documents, research papers, and web articles, transforming them into conversational semantic knowledge via vector embeddings.
Interactive Live Web Sandbox
Interact with the live production deployment directly below across Desktop, Tablet, and Mobile viewports.
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Measurable Impact & Benchmarks
Real performance data and business outcomes achieved for Proprietary AI SaaS Platform.
Query Response Time
Semantic vector retrieval + streaming LLM generation
Retrieval Accuracy
Contextual precision on complex multi-page document queries
Supported Formats
Automated transcript and document parsing
Core Problem & Bottlenecks
Information overload makes recalling insights from dozens of books, videos, and articles nearly impossible using traditional keyword search.
Technical Architecture & Delivery
Built an end-to-end RAG (Retrieval-Augmented Generation) pipeline using Supabase pgvector embeddings, LangChain, and OpenAI / Gemini models with sub-second hybrid semantic search.
Key Functional Capabilities
Key features and engineering highlights implemented
Multi-Format Document Ingestion (YouTube Transcripts, PDFs, Web URLs, Markdown)
Vector Embedding Pipeline with pgvector cosine similarity ranking
Interactive AI Chat Q&A citing exact source timestamps and page numbers
Knowledge Graph Visualization mapping connections between saved concepts
Glassmorphism dark-mode interface with lightning-fast keyboard navigation
Technology & Infrastructure
Modern frameworks and tools selected for this architecture
Frontend & UI
Backend & APIs
Tools & Infrastructure
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