Close the loop with
the physical world.
Models can design experiments in seconds. Augmentiv runs them in real labs and returns structured, verified results your models can learn from.
What does noisy data cost your models?
When a wet-lab result is wrong because of how the experiment was run, your model learns the error. Estimate how much of your data that affects.
Estimates use only your inputs. Execution error rates vary widely by assay and lab — use your own experience. With a run record, execution problems are flagged alongside the data instead of being silently learned.
The bench is the slowest part of the loop.
Wet-lab bottleneck
Designs pile up faster than any one lab can run them.
Noisy labels
When a result is wrong, you can’t tell model error from lab error — so the model learns the noise.
Missing context
Results arrive without the execution details needed to interpret them or train on them.
Ground truth, at the speed of your model.
Submit designs programmatically, run them across a network of verified labs, and get back data with full execution context.
- 01 Active-learning loops Feed verified results straight back into the next round of designs.
- 02 Model validation Test predictions against real experiments, with deviations flagged.
- 03 Benchmark datasets Build training and evaluation sets where every data point has a run record.
- 04 Design–build–test cycles Run many iterations in parallel across labs instead of one queue.
Put your model in the lab.
Talk to us about programmatic access and running your first design batch.