LabWren
LabWren provides a chat-powered scripting tool that automates bench-level protein assay calculations, converting raw absorbance readings into concentration values without fragile spreadsheets or manual copy-paste. The platform generates working, browser-based tools with visible fit statistics and math for every sample, checked against the user's own uploaded data. It integrates Python libraries like pandas and SciPy to fit standard curves and produce accurate results directly from lab data.
- Artificial Intelligence
- AI Agents
- Biotechnology
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Scientists using AI chat assistants for lab code often hit a wall: they can't run the generated scripts, can't trust the results, and can't hand them to a labmate. Labs run on a mess of fragile spreadsheets, one-off scripts, and manual copy-paste, leading to errors and wasted time in routine protein assay calculations.
Solution
LabWren provides a chat-powered scripting tool that turns natural-language descriptions of analyses into working, browser-based tools. Users describe an analysis—such as fitting a BCA standard curve and calculating sample concentrations—and optionally upload a CSV or XLSX example file. The platform builds a sandboxed tool that opens in a browser with buttons and a file picker, shows the fit, the R², and the math for every sample, and is checked against the uploaded example file. The resulting tool can be shared as a link or file that a labmate can use immediately, eliminating the need for fragile spreadsheets or manual copy-paste.
Target Audience
Primary users are bench scientists and lab researchers who need reliable, reproducible protein assay calculations without relying on spreadsheets or manual scripting.
Features
- Natural-language analysis builder that converts chat descriptions into executable tools
- Built-in support for BCA protein assay calculations, including standard curve fitting and concentration determination
- Integration of Python libraries such as pandas and SciPy for curve fitting and data processing
- Transparent output showing R² values, fitted equations, and per-sample calculations
- Browser-based interface with file picker for CSV or XLSX uploads
- Sandboxed tool generation that can be downloaded or shared as a link
- Out-of-range sample flagging with below-LLOQ detection