Announcing Generative AI Customer Case Study: Healthcare Contact Center with auto-MATE In Production

Written by
Jay Acosta
January 25, 2024

Generative AI Customer Case Study: Transforming Healthcare Contact Center with auto-MATE

Link to the case study :


We are proud to announce the publication of a customer case study, showcasing the deployment of auto-MATE, our cutting-edge Generative AI solution, in the healthcare sector in collaboration with our valued customer, MVP Health Care. This partnership, in conjunction with Microsoft's Azure OpenAI, highlights the practical application and profound impact of auto-MATE in revolutionizing healthcare operations.

“Through state-of-the-art auto-MATE features powered by Azure OpenAI, our representatives are now empowered to prioritize meaningful interactions by streamlining administrative tasks, allowing them to devote more energy and attention to building genuine connections with our customers.” - VP Customer Care & Support Services, MVP Health Care. (Established in 1982, MVP Health Care is one of the prominent provider operating in New York, Vermont and New Hampshire)


Launched in April 2023, auto-MATE has rapidly transitioned from an innovative concept to an essential tool for activating unstructured data within and beyond organizations. Leveraging the robust capabilities of Azure OpenAI, auto-MATE has set a new standard in operational efficiency and productivity at MVP Health Care, transcending traditional healthcare processes and setting a benchmark for the industry.  auto-MATE framework brings Generative AI capabilities to enterprise customers via secure integration of Azure OpenAI. It provides pre-built integrations that allow enterprises to quickly connect leading applications such as contact centers, CRMs, Meeting platforms and BI tools, to name a few. The auto-MATE™ framework processes the unstructured Voice, Text, Chat & Video data to extract Insights, automate manual repeat workflows and assist with enterprise decisions. This technology leads to increased efficiency, reduced costs, and improved customer satisfaction, ultimately elevating the overall enterprise productivity.

 90% of enterprise data is unstructured in the form of voice, text and images. Enterprises are looking for new ways to  extract intelligence from this data and automate manual processes. 

Case Study Insights – Real-World Impact and Innovation:

The case study, developed in collaboration with Microsoft, offers a comprehensive exploration of auto-MATE's deployment at MVP Health Care. It provides an in-depth look at the challenges, solutions, and transformative outcomes of integrating Generative AI into healthcare operations.

The Limitations of Traditional AI Techniques In Contact Center before Azure OpenAI:

Before delving into the capabilities of auto-MATE, it's essential to understand the limitations of traditional AI models in the contact center domain. Traditional AI methodologies, including sentiment analysis and topic modeling, while valuable, often provided only a surface-level understanding of customer interactions. These techniques were adept at identifying broad patterns or sentiments but lacked the precision and nuance to understand the deeper intricacies and underlying themes of customer conversations.

For instance, sentiment analysis could tell a company if a customer was unhappy, but it couldn't provide the detailed context about why they were unhappy or offer actionable insights on how to address the issue proactively. Similarly, while topic modeling could recognize the primary subjects of discussion, it couldn't effectively map out the multifaceted web of related sub-topics or the changing dynamics of a conversation in real-time.

Commerce.AI auto-MATE powered by Azure OpenAI:

Commerce.AI has created an automation framework, called auto-MATE for workflows such as call summarization, auto-recommend wrap-up codes, extract sentiment, intents and other pertinent CX insights. It also involves pre-built connections to Microsoft Dynamics  and other CRMs in order to export the structured data such as summaries to the enterprise system of record. MVP Healthcare has chosen the auto-MATE framework to modernize their contact center workflow and automations.  With the deployment of auto-MATE, MVP Healthcare can now leverage  advanced generative capabilities of Azure OpenAI to analyze and comprehend contact center conversations at an unprecedented depth.

Data Sensitivity and Security:

The case study underscores the paramount importance of data security in healthcare. auto-MATE's deployment is marked by its adherence to the highest standards of data protection, ensuring patient information's confidentiality and integrity while harnessing AI's power to streamline operations.

Operational Efficiency and Productivity:

auto-MATE's integration into MVP Health Care's operations demonstrates its capability to enhance efficiency and precision. The solution automates routine tasks, provides actionable insights, and optimizes workflow processes, allowing healthcare professionals to focus on patient care and decision-making.

Ethical AI Implementation:

The case study also addresses the ethical considerations of deploying AI in healthcare. auto-MATE's design and operational framework adhere to strict ethical standards, ensuring that AI's role in healthcare is both beneficial and responsible, with a clear focus on enhancing patient outcomes and care quality.


The release of this case study marks a significant milestone in the journey of Commerce.AI and auto-MATE in the healthcare sector. Our collaboration with MVP Health Care and Microsoft Azure OpenAI exemplifies our commitment to driving innovation and delivering real-world solutions that improve healthcare operations and patient care.

We invite you to delve into the case study to witness the transformative journey of auto-MATE and its role in shaping the future of healthcare with advanced AI solutions.

Return to blog