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Case Study

How a Fortune 50 Healthcare Leader Scaled Agentic AI by partnering with TrueFoundry to build a unified Internal AI Platform.

Executive Summary

NVIDIA is the world's leading supplier of GPUs. With a never-seen-before demand for GPUs globally, the team wanted to improve the performance and utilization of GPU clusters in the data centers. This solution would help them provide GPUs to more clients and improve user experience by reducing the lag time between GPU requests and fulfillment.

For one of the largest healthcare enterprises in the US, digital engagement is mission-critical. Managing over 500 million calls annually across pharmacy, retail, and insurance lines, the organization faced a massive engineering hurdle. They needed to modernize their voice systems from simple menus to intelligent agents capable of handling complex patient intents. While they successfully built an agentic IVR system, the operational complexity of scaling it revealed a gap between innovation and infrastructure. By partnering with TrueFoundry to build a unifiedInternal AI Platform, the enterprise automated their infrastructure, successfully arbitrated workloads between cloud and on-premise, and accelerated their AI roadmap from months to weeks.

The Client: A Pillar of Modern Healthcare

This Fortune 50 Healthcare Enterprise operates at the intersection of retail pharmacy, health insurance, and medical services. Their digital vision is centered on accessibility. Ensuring that every patient interaction is handled with precision and empathy, whether it happens in one of their 9,000 stores or via a digital channel.

The Catalyst: Engineering the "500-Million-Call" IVR

The organization's journey began with a distinct engineering challenge: modernizing their Interactive Voice Response (IVR) system. They needed to move beyond rigid "press 1 for pharmacy" menus to a fully agentic system capable of understanding natural language.

To manage the tension between latency, cost, and accuracy at this scale, the engineering team designed a sophisticated 3-Stage Routing Architecture.

[Placeholder: IVR Architecture Diagram] Visual representation of the workflow: Voice Stream > STT > Guardrails > 3-Stage Routing (Rules/Classifier/Agent) > AI Gateway > LLM Execution.

Technical Nuance: Optimization at Scale

Running this system for millions of users required deep optimization beyond standard model inference:

Latency Reduction

The team implemented global instantiation of agent graphs. Instead of re-creating the agent context for every call, the service maintains active agent graphs that can be reused across sessions. Additionally, prompts are cached for 30 minutes to minimize latency when fetching from the management service.

3-Stage Decision Flow

To preserve expensive GPU compute for complex reasoning, the system uses a tiered approach:

  1. Rule-Based Triage Handles static queries (like store hours) instantly via pattern matching
  2. Scope Classifiers Lightweight models determine domain intent (e.g., "Is this Pharmacy or Insurance?") to prevent routing    errors.
  3. Main Agent Invoked only for complex, in-scope queries. This reduces unnecessary LLM calls by 10-20%.

Safety First

Guardrails are not just an afterthought. They are applied via prompts at the gateway level, ensuring every agent has a fallback mechanism for toxicity or out-of-scope topics before any logic is executed.

The Strategic Pivot:Platformizing the Success

While the IVR architecture was sound, the operational burden of running it was immense. The team faced a "Day 2" reality: managing active-passive reliability across geographically isolated clusters, configuring GPU resources, and handling the disparity between cloud development and on-premise production.

Realizing that manual infrastructure management would stall their roadmap, they utilized TrueFoundry to build a unified Internal AI Platform to serve not just IVR, but all future use cases.

1. From "Cloud-First" to "Best-Infrastructure"

The primary friction point was the divergence between environments. Developers preferred the agility of the cloud, but economic mandates required heavy inference to run on-premise. TrueFoundry provided the abstraction layer that bridged this gap.

  1. Infrastructure Arbitrage: The platform enables the team to utilize NVIDIA NIMs on-premise for stable baselines while bursting to the cloud for peak loads.
  2. Unified Deployment: Developers deploy models to secure, air-gapped on-premise clusters with the same ease as deploying to the cloud.
  3. Zero Ops: By centralizing Kubernetes management within the platform, data science teams no longer manage YAML configurations, freeing them to focus purely on model logic.

2. The AI Gateway: The Central Control Plane

With the system processing over 9 million LLM requests per month, the team needed a robust traffic controller. TrueFoundry's AI Gateway became the central nervous system for their inference stack.

  1. Active-Passive Reliability: The platform manages traffic across geographically isolated clusters (East/West regions). If one region experiences latency, the Gateway seamlessly reroutes traffic to ensure uninterrupted patient service.
  2. Model Independence: The platform decouples application logic from specific model providers. This prevents vendor lock-in and allows the team to swap models instantly as benchmarks improve.

3. Economic Efficiency via Autopilot

To manage the sheer scale of compute required, the platform leverages TrueFoundry’s Autopilot capabilities. Instead of statically provisioning GPUs for peak call volumes, Autopilot automatically scales resources based on real-time traffic demand and orchestrates the use of spot instances for non-critical workloads. This dynamic resource management turned a potential cost center into an optimized asset.

Impact: Velocity, Economics, and Governance

The transition from a standalone IVR project to a comprehensive platform strategy has future-proofed the organization’s AI roadmap.

  1. Production Velocity: Standardization has reduced the deployment time for new agents from months to weeks. Teams can now reuse "global agent graphs" and guardrail configurations across different business lines like Fax Automation and Chat.
  2. Economic Efficiency: By leveraging the platform to move workloads from managed cloud endpoints to self-hosted on-premise GPUs, the organization achieved massive cost avoidance. The ability to right-size infrastructure contributed to a multi-million dollar reduction in projected cloud spend, boosting GPU CAPEX efficiency by over 12%.
  3. Total Governance: Leadership has moved from fragmented visibility to a "Single Pane of Glass." They can now trace every transaction, audit costs per department, and ensure that every interaction adheres to strict healthcare compliance standards.

Conclusion

By codifying the lessons from their massive IVR deployment into a unified platform built on TrueFoundry, this Fortune 50 Healthcare Enterprise has solved the most complex problem in enterprise AI. Day 2 Operations. They have democratized access to state-of-the-art infrastructure for their developers while maintaining the rigorous control required in healthcare. The result is a system that is not only powerful enough to understand half a billion patient voices but efficient enough to do so sustainably.

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