I'm building NeuroFlow, a local-first AI research workspace designed for researchers, PhD students, research assistants, and AI labs who want more control over their research infrastructure.
Today, researchers rely on a fragmented stack of cloud tools, AI assistants, PDFs, notebooks, and disconnected workflows. NeuroFlow is built around the idea that the future of AI research should not require sending sensitive data, unpublished findings, or proprietary research workflows to external platforms. Instead, researchers should be able to own their infrastructure, run models locally, and have complete control over their data, tools, and knowledge.
The goal is to bring the entire research workflow into one environment: searching millions of papers, building interactive knowledge graphs, analyzing research code, discovering connections across literature, identifying research gaps, and generating evidence-backed hypotheses.
The biggest challenge I'm solving is making AI systems that can reason over evidence rather than just generate convincing answers. In scientific research, a claim is only valuable if it can be traced back to the underlying evidence: a paper, dataset, experiment, or piece of code. NeuroFlow is focused on building transparent AI workflows where researchers can understand not only what the AI suggests, but the reasoning and evidence behind every conclusion.
NeuroFlow is an AI powered research workspace built for researchers and students. Search millions of papers, build interactive knowledge graphs, chat with your research, identify promising research gaps, analyze research code, and generate evidence backed hypotheses, all in one local first workspace designed to accelerate scientific discovery.
