About
SciPhys starts with a simple belief: scientific AI should understand the experiment, not just the prompt.
Make experimental data easier to analyze, trust and reuse.
Materials, physics and adjacent instrument-heavy research.
From analysis tool to private lab intelligence layer.
What it does
Experimental researchers spend too much time moving between raw files, plotting tools, reference databases, literature, advisor feedback and writing. The knowledge created during that work often disappears after the paper.
We are starting with high-frequency pain points in materials research: XRD evidence, publication-style figures, lab metadata, natural language search and cross-run comparison.
The best scientific AI systems will be grounded in measurements, uncertainty and domain workflows. They will help researchers ask better questions, not just produce fluent answers.
Workflow
Make daily analysis, figures and interpretation easier.
Preserve project context and advisor visibility.
Create a compounding data layer for experimental materials work.
FAQ
SciPhys is focused on experimental materials and physics workflows, with XRD analysis and lab data management as early anchors.
Yes. A student can use SciPhys as a personal workspace before joining or creating a lab organization.
You can reach the team at hello@sciphys.ai.