How I build AI-powered memory from Telegram chats

The author shares their experience developing an automated service that transforms Telegram chat history into a structured knowledge base. The system operates on a schedule: at night, the server downloads new messages from selected chats, and a dedicated worker processes the data using LLMs (DeepSeek). Information is segmented, extracted as facts, and saved in a searchable markdown format. The primary value of the project lies not just in archiving, but in semantic search capabilities: users can ask about past agreements and receive precise answers linked to specific messages. The author notes that the bulk of the work involved in creating this tool was not related to LLM tuning, but rather to data pipeline architecture, system stability, and API integration. This project demonstrates a practical application of generative AI for personal productivity and knowledge management.