Khaavya Shanthi Loganathan

I led a Salesforce migration project, identifying and addressing inefficiencies in sales workflows, streamlining accountability before implementing automation. I conducted user interviews to uncover pain points, including unbilled change orders, and implemented a feature that alerted teams to ensure accurate invoicing. I built trust with stakeholders through discovery sessions and used Figma to prototype solutions, iterating based on direct user feedback.

Working closely with development teams, I validated concepts in the field, adapting quickly based on live feedback. I integrated Field Service Lightning and Marketing Cloud to support project tracking and customer engagement, ensuring cross-functional alignment. I also evaluated the right Salesforce products based on business needs and ROI, led both onshore and offshore teams, and selected vendor partners. This hands-on, user-focused approach helped reduce manual work by 40% and significantly improved adoption and operational efficiency.

The Salesforce Sales Cloud to Salesforce Health Cloud migration optimized payer management and healthcare operations, achieving a 35% ROI within the first year. I led comprehensive business process mapping to document features accurately, ensuring long-term scalability and alignment with organizational OKRs. I also defined user personas, security roles, and data access policies, enhancing compliance and operational efficiency across sales, claims, and customer support teams.

The project also involved managing cross-functional collaboration, driving stakeholder alignment from business, IT, and compliance teams, overseeing onshore and offshore teams, and optimizing resource allocation and trade-offs to stay within budget. I extensively leveraged Health Cloud’s standard functionalities, reducing customization costs by 40% while delivering an MVP in six months. I improved operational efficiency by 30%, enhanced data accuracy by 25%, and enabled better patient and payer engagement. I also established a requirement traceability matrix to ensure all features aligned with OKRs, contributing to a 20% increase in process automation.

I led the development of an AI/ML-powered intelligent document processing system hosted on AWS, which streamlined underwriting workflows and reduced manual effort by 50%. Previously, underwriters manually reviewed extensive documents, leading to inefficiencies, especially during open enrollment when proposal volume doubled.

The solution leveraged Amazon S3 (AWS S3) for secure document storage and AWS Lambda for real-time data extraction and processing, ensuring scalability and compliance. I collaborated with underwriting, sales, and engineering teams to define business requirements and train the AI model to recognize, classify, and extract key underwriting data. I also established a feedback loop to improve accuracy and ensure compliance with regulatory requirements. We also designed a dynamic rules engine to flag missing data and inconsistencies, reducing underwriting errors by 35%.

The resulting system enabled real-time underwriting criteria validation, reducing back-and-forth communication by 60% and significantly improving turnaround times. As a result, underwriters could focus on complex cases while routine document processing was fully automated.