Case Study 1: AI‑Powered Policy Intelligence for a Global Insurance Provider
Challenge
Client experience teams struggled to quickly locate information buried across hundreds of thousands of policy documents. This created long handling times, inconsistent responses, and operational inefficiency. The company sought a solution that could deliver accurate, trustworthy answers in seconds, without requiring deep system knowledge from support staff.
Approach
Krishna led the design and delivery to build a generative‑AI powered retrieval system using Azure OpenAI Service, Azure AI Search, and supporting Azure components. The team validated a successful proof of concept in two weeks, then deployed the full solution in four months, demonstrating rapid iteration and enterprise‑grade execution.
The solution combined:
- Azure OpenAI Service for natural‑language understanding and grounded responses
- Azure AI Search to index and retrieve 1.3 TB of policy data
- Cloud‑native orchestration to ensure reliability, safety, and accuracy
This enabled support representatives to ask questions conversationally and receive precise, policy‑grounded answers.
Impact
The AI‑powered solution transformed the client‑support experience:
- Support representatives were empowered to resolve inquiries faster, improving both employee experience and customer outcomes.
- The success of the initiative created a foundation for future AI innovation across the organization
95%
accuracy ensured the model consistently returned grounded, policy‑aligned responses, improving confidence in AI‑assisted decision‑making
4–5 seconds
end‑to‑end latency enabled near‑instant answers, elevating both accuracy and user confidence in the system.
1.3 TB
Of policy data search & retrieved
Case Study 2:
Federated GraphQL Modernization for a Global Health Benefits Organization
Challenge
Fragmented APIs and duplicated authorization logic slowed development and degraded user experience across web, mobile, and chat channels. Incremental fixes could no longer meet rising performance expectations.
Approach
Krishna unified the client’s fragmented API ecosystem into a federated GraphQL Supergraph, enabling fast, consistent, and authorization‑aware access to consumer data. Using Aha! for strategic alignment and Rally for execution, he established schema governance and telemetry frameworks to ensure sustainable growth.
Impact
- Unified data contract across all channels
- Scalable foundation for emerging AI‑driven use cases
99.5%
faster developer data access
$1M+
projected infrastructure savings
Case Study 3:
Consolidating Legacy Billing Systems for a Modern Medicare Experience
Challenge
A leading U.S. health insurer relied on multiple legacy billing systems to manage its Medicare Advantage and Medicaid plans. This fragmentation created high administrative costs, inconsistent member experiences, and ongoing reconciliation issues with CMS.
Approach
Krishna used the migration to Oracle Revenue Management & Billing (ORMB) as a catalyst to rebuild the entire revenue‑cycle foundation. The transformation leveraged the following Oracle’s capabilities:
- Consolidation of Legacy Systems
- Member 360 for Transparency & First‑Call Resolution
- Configurable Pricing & Billing Rules
- Automated Reconciliation & Dispute Management
- Streamlined Financial Operations
Impact
- Improved operational efficiency through automated workflows, rules‑based billing, and centralized reconciliation.
- Simplified enterprise operations, reducing manual effort and eliminating system‑to‑system inconsistencies.
- Elevated member experience, supported by transparent billing, accurate subsidies/penalties, and unified Member 360 data.
- Better support for CMS Star Ratings, driven by improved billing accuracy and member satisfaction.
- Stronger scalability, enabling the insurer to adapt quickly to new Medicare and Medicaid requirements.
+$16B (+6%)
2023 → 2024: Premium revenue growth accelerated after Krishna led ORMB consolidation.
+$46B (+16%)
2024 → 2025 : ORMB consolidation year. Krishna unified 5 legacy systems; improved accuracy
14M+
members benefitted, Cleaner member‑level billing
More accurate premium capture
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