ChatSorter

About the startup

ChatSorter is a layered memory API designed to turn conversations into structured, usable data. It works through three core layers: a short-term buffer that captures recent messages for immediate context, a semantic layer that condenses conversations into searchable summaries based on importance, and a fact extraction layer that pulls out, sorts, and stores key structured information like preferences, entities, and relationships with confidence scoring. It also supports custom RAG pipelines, vector database integrations, and ingestion of text and PDF data for handling external knowledge sources. Together, these layers let your AI recall relevant context and maintain long-term memory without bloating prompts, delivering efficient, scalable, and reliable conversational intelligence. ChatSorter produces better results by filtering out conversational noise. Unlike Mem0 or Supermemory, which prioritize massive data ingestion and total recall, ChatSorter focuses on accuracy and precision. Its memory decay on low importance items prevents context bloat, ensuring the AI doesn't get confused by outdated or low-value information.