How a $13B biotech company turned a wall of dead data into a predictive engine for saving lives, with help from Quansight & OpenTeams.
It looked healthy. It had passed every test. Then, at the very last moment—after weeks of preparation—it failed.No warning. No visible defects. Just a quiet blemish, invisible until it was too late. Every failed lung meant starting over. Cost, time, risk—reset.
That lung would never save a life.
One biotech company was building lab-grown, transplant-ready human lungs. And every failure was a mystery buried in a swamp of sensor data.
They knew the signals were in there. But their tools couldn’t see them.
It looked healthy. It had passed every test. Then, at the very last moment—after weeks of preparation—it failed.No warning. No visible defects. Just a quiet blemish, invisible until it was too late. Every failed lung meant starting over. Cost, time, risk—reset.
That lung would never save a life.
One biotech company was building lab-grown, transplant-ready human lungs. And every failure was a mystery buried in a swamp of sensor data.
They knew the signals were in there. But their tools couldn’t see them.
High resolution sensor data
CSV storage across legacy systems
Slow IO | Inconsistent schemas | Manual QA
No reproducibility | ML Blocked | Missed Signals
When Quansight stepped in, we brought a new way of thinking.
Before we talked about AI, we talked about plumbing. Because no algorithm can run on a broken pipe.
Data was transformed into columnar format. Suddenly, insights loaded in seconds, not hours.
Deployed an open source ecosystem of protocols, standards, and tools for an intelligence layer the client owns.
Visualizations snapped to life. It used to take minutes just to show a small portion of data. Now it takes seconds to deeply explore the entire dataset.
Goodbye, dependency hell. Hello, smooth workflows with Conda and Mamba.
Only once the lab could breathe did we turn to prediction.
Once the infrastructure was humming, it was time to ask the question that had haunted the lab for years: Can we predict which lungs will fail?Our data scientists dove in.
They engineered features based on every phase of the decell process.
They built logistic regression and tree-based models to predict three common failure types: blebs, bullae, and blemishes.
They used causal discovery techniques—not just correlations, but models that explained why.
And crucially, they made it interpretable. No black boxes. Every prediction was traceable, understandable, and testable.
A lung failed—just as the model predicted. Only this time, it wasn’t a surprise. Teams could adapt. Investigate. Modify protocols. The team could respond instead of react.
When Quansight stepped in, we brought a new way of thinking.
Before we talked about AI, we talked about plumbing. Because no algorithm can run on a broken pipe.
We delivered clarity because we listened. We met with their scientists to understand what they needed—then we delivered.
When the stakes are this high, black boxes don’t cut it.
You need transparent systems that your team can understand.
You’ve got smart people. But smart people can’t outrun broken systems.
Don’t wait for your next failure to start fixing the pipeline.
