Novaflow is an AI-powered data analysis platform for biology labs that lets life scientists upload experimental data and ask questions in plain English to receive instant, publication-ready plots in minutes rather than months. It addresses the cost and time of bioinformatics analysis through LLM-powered workflows that generate reproducible Python code and Jupyter notebooks. Researchers at institutions including UCSF, Mount Sinai, UC Berkeley, and Harvard are already using the product. Founded in 2025 by a former computational biologist and an ex-Zoom engineer, Novaflow was part of YC's Summer 2025 batch.
Novaflow
ActiveThe AI data analyst for biology labs
Total raised
$500K
1 round
Stage
Seed
Jan 2025
Team
1-10
since 2025
Pricing
—
Founded
2025
San Francisco, United States
Agent-ready
—
Plain-English questions over experimental biology data
Publication-ready plot generation in minutes
Reproducible Python code output for every analysis
Automatic Jupyter notebook generation
LLM-powered bioinformatics workflows
Support for uploading experimental datasets
Designed for life scientists without heavy coding skills
Transparent, inspectable analysis steps
12/100
Early
MCP server
Public API
Webhooks
OAuth 2.0
SDKs
No public agent surfaces detected yet.
Jan 2025 Seed $500K ● Y Combinator
Capital network
$500K raised ·1 backer·10 network links
- Backers1
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- Do I need to know Python to use Novaflow?
- No. You upload data and ask questions in plain English; Novaflow generates the plots while also producing the underlying reproducible Python code and Jupyter notebooks.
- Are the results reproducible?
- Yes. Novaflow generates reproducible Python code and Jupyter notebooks for each analysis, so the work can be inspected, rerun, and shared.
- Who uses Novaflow?
- Researchers at institutions including UCSF, Mount Sinai, UC Berkeley, and Harvard are using the product for experimental data analysis.
- Is Novaflow meant to replace bioinformaticians?
- It targets the time and cost of routine bioinformatics by letting scientists self-serve common analyses; complex studies still benefit from expert review of the generated code.
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