Life Sciences

• Understand, aggregate, and derive insights from proprietary and external data to generate hypotheses focused on clinical development and precision medicine approaches
• Enable analysis on unconventional and unstructured data from varied sources – files, images, EMRs, devices, wearables and sensors, social media listening
• Strategies for patient segmentation, subpopulations, patient enrollment and engagement –
• AI-driven clinical trial insights for study monitoring to improve accuracy of RBQM and faster risk prediction/ identification
• Insights and interpretation for future protocol design

• Molecule screening and compound-target outcome prediction
• Drug design and optimization based on pattern recognition
• Compound/ molecule library assessment, selection, re-evaluation/ repurposing
• AI model for data testing – cellular response, end point prediction, biomarker models, and analysis
• Predictive drug properties, bioactivity, therapeutic response, potency, and toxicity

• AI-powered marketing intelligence
• Personalized and dynamic HCP communications and content
• AI-driven omnichannel and next best action programs
• AI powered MSL apps and feedback tools
• HCP and patient journeys – persona analysis, behavior prediction
• ML models for personalized and precision medicine inputs
• Channel analysis – RoI, performance, effectiveness

• Manufacturing process optimization, bottleneck identification, outcome prediction of corrective actions
• Manufacturing performance – yield, quality, capacity management insights
• Monitoring in-line manufacturing processes
• Predictive quality management
• Supply chain management, supplier reliability, and inventory optimization
• AI enabled Integration of manufacturing, logistics, and sales for optimizing demand management and operations

• Drug discovery scenarios
• Wearables and sensors in trials
• Site and subject screening/ monitoring

• Wearables, remote devices for patient monitoring
• Smart devices and mobility devices for patient engagement
• Smart pills – ingestible and implantable sensors
• Treatment compliance

• IQ ensures successful and right installation of software, build verification, and smoke tests.
• OQ tests key operational and security functions.
• PQ validates performance gaps and issues impacting business, user experience.

Our Key Considerations

• • Architecture review and changes – Component/Cluster/SOA model
  • Deployment plan – Multiple servers and web farm
  • Code structure – Structure of the core code, code generation, code integration
  • Environment – Scaled down environment (extrapolate through modelling techniques), types of environment (dev, pre prod and prod)
  • Functional stability – Major functional defects to be closed, all UI defects to be closed

• Analysis of the applications in scope – technology stack, users, business criticality, current automation levels, etc.

Tool Selection

• Evaluation of tools & fitment analysis – ROI, tool reusability, tool maintenance.
• Efforts for team up-skilling.
• Provide tool recommendation report.

Automation Framework

• Design and create an automation framework broadly covering the processes.
• Documentation covering process and framework reusability.

Pilot

• Define success criteria for Pilot.
• Choose an application/module for Pilot – a representation of the application environment.
• Execute the Pilot and demonstrate the outcome.

Execution

• • Mainstream QA of the applications through automation using the tools.
  • Maintenance of scripts in the repository for reuse.
  • Gauge performance metrics.

• Continuous testing that plugs right into DevOps tooling
  

  Test Creation

  • New functionality and regression testing.
  • ML & cognitive generation to instantly produce thousands of tests.

  Test Execution

  • Wide variety of tests across functional, unit, performance, load & security.
  • Lightning-fast test runner – test across 1000’s of browsers/multiple devices at once.
  • Unique write-once capability.