Phylo

What is Phylo?

Phylo is an applied AI research lab that has developed Biomni Lab, the first Integrated Biology Environment (IBE) designed specifically for researchers. It functions as a unified workspace where biologists can collaborate with autonomous AI agents to plan experiments, analyze complex datasets, and interpret results without switching between fragmented tools. The platform natively integrates over 120 software packages and 70 databases, enabling it to execute sophisticated workflows automatically. By providing scientifically rigorous, traceable, and hallucination-checked outputs, Phylo aims to compress research timelines from weeks to minutes.

How to use Phylo?

To use Phylo's Biomni Lab, researchers log in to the web-based platform and interact with the AI agent via a natural language chat interface. You can upload biological data files or simply state your research objective, such as 'analyze this RNA-seq dataset' or 'find structural homologs for this protein.' The agent will then plan the workflow, select the appropriate integrated tools, execute the analysis on scalable cloud infrastructure, and present the results with full code transparency and citations for verification.

Phylo's Core Features

• Integrated Biology Environment unifying agents, tools, and databases in one workspace.

• Natural language interface for commanding complex biological research tasks.

• Native access to over 120 biology software packages and 70 databases.

• Scientifically rigorous AI that scans for hallucinations and provides citations.

• Scalable cloud infrastructure supporting large files and GPU-accelerated computing.

• Personalized memory that learns and adapts to researcher preferences.

• Transparent execution traces allowing users to verify every step of the analysis.

• Secure, sandboxed environments ensuring data privacy for each session.

• Automatic code generation and execution for reproducible science.

Phylo's Use Cases

• #1

  Automating complex bioinformatics pipelines like multi-omics data analysis

• #2

  Designing, simulating, and visualizing protein structures

• #3

  Conducting comprehensive literature reviews with automatic summarization

• #4

  Planning laboratory experiments and identifying necessary reagents

• #5

  Validating research hypotheses by cross-referencing global biological databases

• #6

  Debugging and generating code for biological data processing

• #7

  Scaling computation for memory-intensive research tasks