High-throughput antibody engineering lives or dies on data integrity. We built a single hub for molecule design, lab execution, and experimental results, wired straight into the instruments, so no data point ever gets lost or misattributed.
At BigHat, we intentionally built our infrastructure to move fast. The MillinerTM platform is the engine at the heart of our work – an AI-driven system where computational design and high-throughput wet lab experimentation enable a continuous cycle of learning. Every design is evaluated across a broad panel of automated assays. The data shapes the next design. It’s a closed loop, running continuously.
Sitting at the center of that loop is ReccyTM. Think of it as mission control for our scientists: the single interface through which they design antibody candidates, direct samples through the lab, and track every result from synthesis to characterization.
But running a high-throughput lab at scale surfaces a deceptively hard problem: data integrity. Every experiment generates layers of information – barcodes, reagent lots, instrument outputs, analytical results – and all of it has to be captured correctly, every time, at volume. A missed association or manual transcription error can lose a data point and quietly corrupt an experiment.
So BigHat’s Automation team built tooling to eliminate that risk. Reccy is directly embedded into our lab infrastructure. Experimental designs translate automatically into machine-readable instructions. As runs execute, data flows back and forth and binds itself to the right experiment. No manual handoffs, no gaps in the chain of custody.
The result is a platform where design, execution, and data collection are seamlessly connected. That integrity at scale is what allows us to move from hypothesis to insight to development candidate without losing fidelity along the way.
