The Foundation for Pharma Success
Purpose-built for pharma, the AutoData Foundation serves as the backbone of Verix’s data intelligence platform, automating the ingestion, integration and preparation of data for AI/ML modelling and GenAI.
Learn MoreTurn Data into a Competitive Advantage
Streamline Data Utilization
Automatically standardize and unify any number of data sources into a centralized foundation, creating a 360* view of the therapeutic landscape within hours.
Reduce Time to Insights
Significantly reduce the burden, time and costs of data preparation for seamless data analytics and AI-driven insights.
Deliver at Scale
Effortlessly consolidate data via a flexible no code platform that allows for easy scalability across brands, functions and use cases.
A New Era of Precision
Consolidate disparate data sources into a a rich feature store with hundreds of attributes across HCOs, HCPs, and patients. This powerful integration empowers precision medicine and specialty brands to execute hyper-targeted strategies, redefining what’s possible in commercial success.
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Unleash the potential of data to accelerate growth
Ingestion
Integrate any number of data sources including CRM, APLD, NPP, specialty data
Define strategic market definitions via a smart no-code configuration wizard
Automatically scan and fix duplicates, data anomalies and integrity issues
Processing
Operationalize data, transforming thousands of features into 360 data tables for HCOs, HCPs, Patients and Territories
Out of the box predictive
results and automatically segment HCP universe
Extract actionable AI/ML insights, visualize key metrics and empower commercial teams with data-driven solutions
Better Data for Better Results
- Quick deployment, reducing implementation times by <70%
- A flexible and repeatable approach that allows for easy scalability
- Easy to use no code interface for fine-tuning of business logic to brands objectives
- Seamlessly ingest and refresh data to capture changes in the therapeutic landscape
- Effortlessly embed predictive measures to prepare data for complex decision-making mechanisms
- Long implementation times, often spanning months or years
- One and done projects that require significant manual effort to build and run
- Limited flexibility to adapt to evolving business needs
- Heavy reliance on IT, and data engineers for updates and maintenance
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