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Assembly
Plants already collect sensor histories, vibration recordings, camera images, maintenance logs and QC inspections. Turning that into a working model normally takes an ML team weeks. With Assembly, an operator writes something like: > Prevent unplanned stoppages on Press Line 3 and stop defective metal nuts from reaching assembly. > Max 1 false alarm per shift; catch at least 90% of failures and defects. The agent then: 1. Formulates concrete ML tasks with measurable KPIs (e.g. "will this press fail within 30 cycles?", "where is the defect on this part?"). 2. Discovers which plant streams matter for each task. 3. Designs each dataset by writing its own `prepare.py`; validators check it for leakage and sanity before an operator approves it. 4. Researches models in an autoresearch-style loop: it explores the data, plans experiments, writes train.py, runs it under a time budget, and keeps a change only if fixed scoring code, comparing against labels the agent never sees, says it's better. 5. Ships the winner: full training, a final gate on a hidden test split, packaging and a live endpoint.
18 Oct 2026
