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Workflows, redesigned

Five scientific workflows, from experiment runs to validation planning: what was slow, what we built, and what changed.

Case Study · Analytical Development

Full Experiment Lifecycle Automation

Mid-size biotech, analytical development team of 6

The problem

Scientists ran every analytical method by hand, from setup through data processing to the final report. Taking one assay run from setup to a reviewed report could take 5–8 days, most of it spent processing data, analyzing results and writing the report.

How it works

Execution

Reads method parameters, monitors the instrument, collects and pre-processes data, flags anomalies as they happen.

Review & Report

Checks data completeness against acceptance criteria, auto-generates the standardized report. Scientist reviews and signs.

Design principle

Every AI action previews before it writes anything. Final sign-off always stays with the scientist — not a technical limit, a trust and compliance requirement.

Results

30–65%

reduction in time per experiment run

5–8 days → ~1–3 days

per run, setup to reviewed report

>70 files

instrument outputs auto-classified per run

4–8 wks

from concept to stable first-method rollout

Fits teams like

Repetitive experiment workflowsHas data review + reporting overheadQuality control labsClinical data analysis

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