Lab Loop represents research as a structured process (hypotheses, designed experiments, evidence and conclusions) rather than a pile of runs, notebooks and dashboards.
Group related experiments under a shared goal, with explicit constraints and a budget that the loop respects.
Each hypothesis carries the criterion that would refute it, stated before any experiment runs.
Trial configurations are produced by search methods and recorded with their full auxiliary bundle.
Code, environment, seeds and data splits are captured so a result is attributable to the varied factor.
The state of belief over a programme is tracked and updated by each experiment, not reduced to a single number.
When a programme closes, the conclusion is fixed and traced to the evidence and trials that produced it.
A run in a tracker is one observation. Lab Loop keeps the loop that gives runs their scientific meaning: which hypothesis they test, what they were controlled against, and what belief they changed.
Run programmes of experiments that accumulate evidence, instead of managing isolated runs by hand.
Drive the same loop from applications and automation without reimplementing its governance elsewhere.
Lab Loop is designed to sit inside a broader research estate rather than replace it, composing with the compute, trackers and registries you already use.