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Why We Built Pipette (Even Though Claude Code Exists)

A candid look at when AI coding assistants fall short for bioinformatics—and what we're doing about it

Jan. 14 2026

Yes, Pipette is powered by Claude. And yes, Claude Code is an excellent tool that can write and execute bioinformatics code from your terminal. So why did we build an entirely separate platform?

Because tools and scientists have different needs.

The problem we kept seeing

For over 20 years, I've worked in bioinformatics—from plant genomics to brain single-cell research to clinical applications. The pattern is always the same: a wet-lab scientist generates beautiful data, then waits weeks or months for someone to analyze it. The actual compute time? Often just hours.

The bottleneck isn't the biology. It's access.

Claude Code is genuinely powerful. If you're comfortable in a terminal, can manage Conda environments, debug Docker containers, and know which tool to use for your specific analysis type, it will absolutely help you move faster.

But that's a big "if."

What Claude Code assumes you have

Claude Code is a general-purpose coding assistant. It assumes you bring:

For computational biologists, these assumptions hold. For the PI trying to look at their own RNA-seq data? They're dealbreakers.

Where Pipette fills the gap

We built Pipette for the scientist who shouldn't need to become a software engineer to analyze their own data. Here's what that means in practice:

Managed infrastructure. Your data lives on cloud servers. You don't configure anything. You don't debug STAR installations. You just ask your question and get your answer.

Domain expertise encoded. Pipette knows which tools to use for differential expression, which databases to query for pathway analysis, and what quality control steps your single-cell data needs. That knowledge is baked in—not something you need to specify.

Provenance for reproducibility. Every analysis step is tracked. When reviewers ask how you generated Figure 3, you have an answer that satisfies publication standards.

Compute that scales. Running alignment on 50 samples? Pipette handles the orchestration. You don't provision instances or monitor jobs from your terminal.

Outputs ready for manuscripts. Reports and figures formatted for publication. Share with collaborators without exporting from a Jupyter notebook.

Access from anywhere. Check on your running jobs from your phone. No SSH required.

Same engine, different vehicle

Pipette and Claude Code share the same underlying intelligence. We're not competing with Claude—we're building on it. Think of it this way: Claude is a powerful engine. Claude Code puts that engine in a race car built for developers. Pipette puts it in a vehicle designed for biologists.

The question isn't which is better. It's which fits your workflow.

If you're a computational biologist who lives in the terminal and enjoys configuring environments, Claude Code is fantastic. Use it.

If you're a wet-lab scientist who wants to go from data to insight without becoming a DevOps engineer, that's who we built Pipette for.

The real goal

We didn't build Pipette to replace bioinformaticians. We built it to eliminate the waiting—the months where great science sits in a queue because there aren't enough analysts to go around.

Your postdoc shouldn't be your bottleneck. Your IT infrastructure shouldn't be your bottleneck. The only thing slowing down your science should be the science itself.

That's what we're building toward.

Pipette is currently in early access. If you're a biologist who's tired of waiting for your data to be analyzed, we'd love to hear from you.