Generic LLMs provided by companies like OpenAI, Anthropic, and Gemini, offer broad capabilities but often fall short in addressing the unique, nuanced needs of vertical industries.
Traditional tools in knowledge scope like Google Scholar effectively address the issue of information asymmetry by providing quick access to a vast array of academic resources, enabling users to find relevant materials much faster than before. However, this benefit comes with a new challenge: information overload or information explosion. With an overwhelming number of search results and materials to sift through, users often struggle to identify the most valuable and contextually relevant sources.
TXYZ, a research AI, is grounded in massive peer-reviewed papers, which represents the highest complexity and authority of human knowledge. TXYZ aims to build an interactive platform to assist researchers in their full research lifecycle. Long term wise, imagine that every researcher can come to TXYZ and “teach” AI their knowhow in research, these knowhow is distilled as agentic workflows and can be shared and used by their peers, what’s more, researchers can build agents on top of each other in a “stackable” fashion, and those knowhow which are previously stored only in human brains can then be transferred into AI. Eventually, TXYZ will be a Human-AI network of intelligence, and going even further, the interactive data that gathered from knowledge workers all over the world may help to train to build the next generation of AI which can think and do reasoning, even doing original scientific research on its own, namely an AI scientist.
“We realized the core value of AI is reducing the complexity of information rather than breaking information asymmetry which the internet and mobile internet do.”, said Bojun Yan, the CEO of TXYZ.