Potato

Potato is an AI-powered scientific execution platform built for life science researchers who want to design and run experiments faster and more effectively. At its core, Potato combines literature search and synthesis, scientific computational tools, and an AI co-scientist called Tater to support end-to-end experimental workflows — from early ideation through data analysis and reporting.

Potato is built for a broad range of R&D professionals across academia and industry, including wet lab biologists developing workflows outside their core expertise, computational biologists comparing tools and parameters in parallel, data scientists and bioinformaticians adopting new analytical methods, and automation engineers looking to translate protocols into robotic scripts more efficiently.

Core Workflow: How Potato Works

1. Give Tater a project. Users can upload a paper, dataset, or describe their scientific need in plain language — for example, analyzing data, troubleshooting an experiment, engineering a protein, or designing an assay.
2. Plan and refine. Drawing on an extensive literature database, Tater translates the user's intent into a reproducible workflow grounded in the latest scientific knowledge from the relevant field. Users can review and refine the details throughout this process.
3. Execute experiments. Tater applies computational tools, delivers detailed protocols, and can convert them into robot-ready scripts for automated laboratory systems. It is also capable of analyzing raw results and generating figures.
4. Review results and iterate. Each run produces a final report documenting every step Tater took. From there, users can explore variations, alternative next steps, or entirely new strategies in parallel.

Key Capabilities

• Literature search and synthesis: Potato searches and synthesizes scientific literature to inform experimental design and ensure workflows reflect current knowledge in the field.
• AI co-scientist (Tater): Tater acts as a collaborative AI partner capable of guiding projects from initial concept through execution, analysis, and reporting.
• Protocol generation: Potato produces highly accurate protocol drafts, reported to save researchers 8–12 hours of manual work per protocol, requiring only around 45 minutes of review and polishing.
• Automation-ready outputs: Protocols can be translated directly into scripts compatible with robotic and automated laboratory systems.
• Data analysis and figure generation: Tater can process raw experimental results and deliver publication-ready figures.
• Parallel exploration: Potato supports branching into hundreds of experimental possibilities simultaneously, enabling faster discovery and innovation.

How Potato Helps Research Teams

• Explore new ideas: Potato helps researchers move past the hardest first step by rapidly shaping early-stage ideas into executable plans and surfacing new insights across a wide solution space.
• Accelerate projects: By streamlining experimental design, workflow execution, and data analysis, Potato frees researchers to focus on scientific discovery and innovation rather than operational bottlenecks.

Security and Data Privacy

• User data ownership: Users retain all rights to everything they upload, including papers, data, and prompts. These are not shared outside the user's private workspace. LLM-generated content is fully owned by paid account users, while free users retain the right to use generated content internally.
• Secure by default: Potato is designed with modern security best practices. Uploaded information is only accessible to users within the user-controlled Workspace and can be deleted at any time.
• No training on user content: Potato explicitly prohibits using uploaded or generated content from paid accounts to train or improve its AI models, ensuring that what users share remains private.

Potato offers both free Open Access and paid plans with team and premium features, making it accessible to individual researchers as well as larger R&D organizations across academia and industry.