https://catalogartifact.azureedge.net/publicartifacts/ltim.ltimindtree_data_and_ai_rxintelle-8d1b18bc-eab9-44b3-9837-c41644e1888b/image1_LTM216X216px.png

LTM Data And AI RxIntelle

LTIMindtree Limited

Prezzi Gratuito

LTM’s AI-powered RxIntelle delivers actionable commercial insights through natural language interaction with enterprise data, helping teams Outcreate decision-making for sharper execution.

LTM + Microsoft Fabric Data Agent provides a suite of commercial agents designed to retrieve, analyze, and summarize data from multiple sources, delivering enterprise-grade insights in seconds, not weeks.

With these agents, you can:

  • See prescribing and market signals clearly: Gain a better understanding of healthcare provider (HCP) prescribing behavior, channel preferences, patient populations, and more.
  • Ask questions the way you naturally work: Interact with enterprise data using natural language.
  • Move from insight to action together: Enable AI workforce collaboration to solve real business problems.

    • Business Challenge

  • Disconnected data limits commercial visibility: Fragmented data sources reduce end-to-end visibility of physician activity and brand performance.
  • Insights stay trapped across dashboards: Business leaders need to analyze and navigate multiple dashboards to obtain relevant commercial insights, which can be time-consuming and require significant effort.
  • Reporting queues slow decisions: High dependency on analytics teams to generate custom reports introduces data bottlenecks that slow down decision-making and reduce organizational agility.

    • Solution Approach

  • Bring commercial intelligence into one view: Built using Microsoft Fabric and Azure AI Foundry, LTM’s pre-built commercial suite of agents enables market access teams, marketing leads, and brand managers to get actionable insights into the entire commercial data landscape in a single place.
  • Combine internal and external signals responsibly: Using an agentic AI framework, the team of agents gathers and summarizes information from internal data sources such as HCP-level prescription activity datasets, claims, and engagement metrics, as well as external sources such as public health websites and publications.
  • Coordinate tasks with clear orchestration: The planner agent orchestrates and manages the end-to-end workflow consisting of tools and sub-agents that specialize in distinct components such as text-to-SQL parsing, searching websites, and generating summary reports.
  • Tailor responses to life sciences context: These agents are customized for the life sciences industry, enabling the agent to interpret business questions accurately.
  • Fit into everyday collaboration tools: It can be easily integrated with Microsoft Teams, Microsoft 365 Copilot Studio, and custom web applications.

    • Benefits

  • Accelerate speed-to-insights for commercial decisions: Get answers to complex, domain questions in seconds, not weeks.
  • Enable self-serve analytics without code or ticket queues: Empower business teams to perform data analytics with no code required.
  • Expand access across the channels teams already use: Launch the agents into numerous channels, including Microsoft Teams and Microsoft 365 Copilot.
  • Extend and connect your agent ecosystem as needs grow: Connect Data Agents to existing agents built on Azure AI Foundry or Microsoft 365 Copilot Studio to enable AI workforce collaboration.

    • For example, commercial agents can help sales and marketing teams generate natural language responses to questions about patient populations, high-value physicians, and patient journeys. The agents help in:

  • Target the right physicians using privacy-safe insights: Identifying potential prescribers based on patient population using de-identified patient health data.
  • Refine engagement using real prescribing and interaction signals: Optimizing HCP engagement and targeting strategies based on prescribing behavior and engagement metrics.
  • Improve spend efficiency while protecting revenue outcomes: Improving resource allocation to drive cost efficiencies and maximizing net revenue.

    • In uno sguardo

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