Novo Nordisk
Clinical data analysis
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
- 50+ ideas evaluated per quarter vs 5-10 previously
Manual medical drafting and code translation bottlenecked clinical experts. Now, secure AI generates first drafts for human review.
A global biopharmaceutical organization developing treatments for severe neurological and immunological conditions must manage rapidly growing data volumes within a highly regulated environment.
Clinical teams faced heavy administrative burdens, from generating first drafts of medical documents to manually translating large volumes of...
“AI is an enabler for UCB. It helps us be more productive, but it also opens up completely new possibilities in research and development.”
Biopharmaceutical company focused on neurology and immunology treatments.
Enterprise software, cloud infrastructure, and consumer electronics platform.
UCB's Clinical research support is part of this use case:
Related implementations across industries and use cases
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Writers averaged 2.3 reports/year. Teams now generate 300-page regulatory drafts in minutes, cutting writing time by 90%.
Consultants manually scoured knowledge bases. Now, they prompt an internal AI to synthesize insights into structured, on-brand drafts.
Every update meant re-recording across five teams. Now a script upload produces multilingual medical video without a single studio session.
30-minute report-digging kept fleet audits chronically deferred. Now one prompt delivers every device, flag, and follow-up emails at once.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Manual medical drafting and code translation bottlenecked clinical experts. Now, secure AI generates first drafts for human review.
A global biopharmaceutical organization developing treatments for severe neurological and immunological conditions must manage rapidly growing data volumes within a highly regulated environment.
Clinical teams faced heavy administrative burdens, from generating first drafts of medical documents to manually translating large volumes of...
“AI is an enabler for UCB. It helps us be more productive, but it also opens up completely new possibilities in research and development.”
Biopharmaceutical company focused on neurology and immunology treatments.
Enterprise software, cloud infrastructure, and consumer electronics platform.
UCB's Clinical research support is part of this use case:
Related implementations across industries and use cases
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Writers averaged 2.3 reports/year. Teams now generate 300-page regulatory drafts in minutes, cutting writing time by 90%.
Consultants manually scoured knowledge bases. Now, they prompt an internal AI to synthesize insights into structured, on-brand drafts.
Every update meant re-recording across five teams. Now a script upload produces multilingual medical video without a single studio session.
30-minute report-digging kept fleet audits chronically deferred. Now one prompt delivers every device, flag, and follow-up emails at once.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.