I designed and built a RAG-powered AI knowledge platform that allows businesses to search across documents, internal knowledge bases, websites, databases, and connected tools using natural language.

The system ingests documents from multiple sources, extracts and cleans the content, splits it into optimized chunks, generates embeddings, and stores them inside a vector database for semantic search. When a user asks a question, the platform retrieves the most relevant knowledge, sends the context to an AI model, and generates accurate, source-grounded answers instead of relying only on the model’s general knowledge.

• RAG architecture design and development • AI-powered document search systems • PDF, DOCX, TXT, CSV, web page, and knowledge-base ingestion • Document chunking and embedding pipelines • Vector database setup and semantic search • OpenAI and LLM integration • Source-grounded AI answers with citations • Internal company knowledge assistants • API, CRM, Slack, Notion, cloud storage, and database integrations • Analytics, monitoring, and knowledge-search dashboards

The solution gives teams instant access to business knowledge, reduces time spent manually searching through files and systems, improves answer consistency, and helps automate repetitive support and research tasks. It can be used for internal knowledge assistants, customer support, policy search, technical documentation, employee onboarding, document intelligence, and AI-powered decision support.

The architecture is designed to be secure, scalable, and flexible, allowing additional documents, users, data sources, and integrations to be added as the business grows.

Tech Stack: OpenAI, LangChain, RAG, Embeddings, Vector Databases, Pinecone, Qdrant, Weaviate, FAISS, Chroma, Python, FastAPI, PostgreSQL, APIs, Cloud Storage, Semantic Search.