Platelet (platelet.io) is a company I founded to fix a problem I saw repeatedly in early discovery research: getting from raw experimental data to a trustworthy, presentable result takes far more effort than it should.
The Problem
The normal workflow for a biologist doing data analysis looks something like this: clean and organize data in Excel, run statistics in GraphPad or R, generate figures in Python, and finally assemble everything into a slide deck in PowerPoint. Each step means exporting, reformatting, and re-checking data as it moves between fragmented, disconnected tools.
On top of that, most biologists are classically trained in statistics rather than as statisticians. That gap leads to inconsistent choices in test selection, corrections, and reporting, which means the same dataset can be analyzed a dozen different (and sometimes contradictory) ways depending on who’s running the numbers.
The result is hours of manual data manipulation and a nagging uncertainty about whether the analysis is actually right.
Our Belief
At Platelet, we believe all data is good data. Researchers shouldn’t have to second-guess whether their statistics are sound, or burn hours reformatting the same dataset across five applications just to produce a figure they can put in a paper.
What We’re Building
Platelet is a single platform that replaces that fragmented workflow, taking researchers from raw data straight to presentation- and publication-ready graphs. It gives immediate insights through advanced visualizations and data exploration tools, including interactive charts, graphs, and statistical analysis, all optimized for rapid processing and real-time results, so there’s no more waiting on exports between apps to see where an analysis stands.
Because early discovery data is often sensitive and unpublished, privacy is built into the platform rather than bolted on: research data stays on the user’s device and is never stored or shared, so teams get complete privacy and security for their work. Built-in collaboration tools let researchers share experiments and findings with their team without giving up that data sovereignty.
This is very much an ongoing effort, but the mission stays the same: make robust, reproducible statistics the easy default for biology and chemistry research, not the exception.