The Research Productivity Challenge
Scientists at Genmab needed self-serve insights from clinical trial data but were spending excessive time on technical tasks instead of research analysis.
Complex SQL Requirements
Scientists spent days writing complex SQL queries to extract insights from clinical trial databases, diverting time from actual research analysis.
Manual Visualization Creation
Creating data visualizations required significant technical expertise and time, slowing down the hypothesis testing process.
Delayed Research Cycles
The technical overhead of data access and visualization creation significantly extended research timelines and delayed drug development.
Limited Data Access
Only scientists with advanced technical skills could access and analyze the clinical trial data, creating bottlenecks in research teams.
The Multi-Agent AI Solution
Anpu Labs developed a sophisticated multi-agent AI platform with specialized agents for SQL generation and data visualization, enabling natural language queries.
Natural Language Query Agent
Scientists can request analyses using plain English, with the AI agent automatically translating requests into optimized SQL queries.
- Natural language processing for query interpretation
- Automatic SQL query generation and optimization
- Context-aware clinical trial domain knowledge
Intelligent Visualization Agent
Automatically generates appropriate visualizations based on data types and research context, with customization options for scientific publication.
- Automated chart type selection based on data characteristics
- Scientific publication-ready visualizations
- Interactive dashboards for deep analysis
Insight Explanation Agent
Provides contextual explanations of findings, statistical significance, and potential research implications in clear, scientific language.
- Statistical significance analysis and interpretation
- Research context and implications
- Hypothesis validation and next steps recommendations
Revolutionary Research Impact
75%
reduction in hypothesis testing cycles
Democratized data access across research teams
Increased insights generated per scientist
Accelerated research timelines
Natural language query interface
Automated visualization generation
"The AI agents have transformed how our scientists interact with clinical trial data. What used to take days of SQL writing and visualization work now happens in minutes through natural language requests."
Tine Casneuf
Genmab
Multi-Agent AI Architecture
The multi-agent platform architecture features specialized LLM-powered agents for query generation, data visualization, and explanation, all operating within a secure, compliant environment.
Query Agent
Translates natural language to SQL
Visualization Agent
Generates publication-ready charts
Insight Agent
Explains findings and implications