Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
A pharmaceutical enterprise built a cloud-based research foundation harmonizing more than 200,000 patient-years of clinical trial data across studies, disease areas, and research programs.
Extracting insights from clinical data depended heavily on scarce specialist expertise and time-intensive manual workflows. Teams spent months...
“The pharmaceutical industry is under enormous pressure to improve productivity.”
Pharmaceutical company specializing in diabetes, obesity, and rare blood disorders.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Novo Nordisk's Clinical data analysis is part of this use case:
Related implementations across industries and use cases
Silos forced teams to manually chase data. Now, AI agents identify adverse events in seconds, eliminating weeks of cross-referencing.
Paper recipes took months to digitize; each new regulation meant combing the library for impact. AI agents do both now—engineers review.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
A maze of disconnected systems obscured global trial visibility. Now, a digital twin guides AI to automate heavily regulated workflows.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
A pharmaceutical enterprise built a cloud-based research foundation harmonizing more than 200,000 patient-years of clinical trial data across studies, disease areas, and research programs.
Extracting insights from clinical data depended heavily on scarce specialist expertise and time-intensive manual workflows. Teams spent months...
“The pharmaceutical industry is under enormous pressure to improve productivity.”
Pharmaceutical company specializing in diabetes, obesity, and rare blood disorders.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Novo Nordisk's Clinical data analysis is part of this use case:
Related implementations across industries and use cases
Silos forced teams to manually chase data. Now, AI agents identify adverse events in seconds, eliminating weeks of cross-referencing.
Paper recipes took months to digitize; each new regulation meant combing the library for impact. AI agents do both now—engineers review.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
A maze of disconnected systems obscured global trial visibility. Now, a digital twin guides AI to automate heavily regulated workflows.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.