San Francisco, CA – 18 May 2025 — Scientific R&D is accelerating, but the systems supporting it are outdated, fragmented, and painfully manual. Biotech teams still rely on duct-taped workflows — protocols in Google Docs, timelines in Gantt charts, data in spreadsheets, and context scattered across Slack. The result? Slower research, costly inefficiencies, and over $50 billion lost annually to irreproducible experiments.

Labtree is a new kind of platform built to change that — combining structured execution, real-time collaboration, and a proprietary AI engine purpose-built for how science actually works. Today, we’re announcing our emergence from stealth and a pre-seed funding round backed by early investors and operators from Linear, Airtable, Google, and the University of Cambridge.

The infrastructure layer for biotech

Labtree isn’t another ELN or project tracker. It’s the operating system for scientific teams — a platform where planning, execution, communication, and learning happen in one unified space. Scientists can run experiments, troubleshoot failures, track versions, and share context seamlessly — all without switching tools or losing information in handoffs.

Our system doesn’t just record what happened. It understands how and why it happened. That context is everything when you're optimizing a CAR-T construct or troubleshooting a failed fermentation run.

This is more than digitization. It's a shift in how biotech companies scale their research operations.

“We knew we weren’t the only ones struggling with this. Everyone was reinventing the wheel — wasting time, duplicating work, and making preventable mistakes. Labtree is our answer.”

Hanna Luniak, Co-founder

AI that reasons, not just summarizes

At the core of Labtree is our proprietary AI engine — trained not just on public papers, but on the internal logic of scientific work: experiment trees, protocol deltas, failure patterns, and structured metadata. It’s built to assist where other copilots fall short — offering suggestions grounded in research logic, not generic text generation.

From intelligent experiment design to failure analysis and research memory retrieval, Labtree AI is embedded directly into scientific workflows. It surfaces connections between seemingly unrelated trials, flags inconsistencies before they cascade, and turns trial-and-error into learnable, repeatable progress.

Where others generate content, we enable discovery.

Built from the inside

Labtree was born from lived pain — in academic labs, biotech startups, and consulting projects for R&D teams. We saw the same issues everywhere: scattered files, lost knowledge, fragile coordination. We also saw how teams tried to patch it together with a mix of productivity tools that weren’t built for science.

“We’re building infrastructure for the next generation of biotech companies. Software that understands scientific work at the systems level — not just as isolated notes or static records.”

Matt Ciarkowski, Co-founder

What’s ahead

The platform is currently in private beta with a select group of early-stage and public biotech teams working in synthetic biology, oncology, and diagnostics. A broader release is planned for later this year.

Over the coming months, Labtree will continue expanding its proprietary AI systems, evolving the platform to support increasingly complex scientific workflows — online, offline, and at the bench. We’re just scratching the surface of what’s possible.

Scientific research is vast, complex, and evolving — and so is our roadmap. We’re just getting started.

To our early supporters: thank you for your belief in the mission. If you're building at the frontier of biotech, or want to help shape the infrastructure behind it, we invite you to join us.

For more information, visit labtree.io or contact press@labtree.io.