‍The Challenge: A Costly Snowflake Migration 

Pharma Logistics is a 30-year old pharmaceutical returns company, the critical intermediary that manages the return of expired, recalled, or overstocked drugs on behalf of pharmacies, hospitals, and healthcare systems. The business runs on precision: regulatory compliance, accurate credit recovery, and airtight data. 

When they made the decision to migrate their data stack to Snowflake, they expected performance gains and cost predictability. What they got instead was a monthly cloud bill that kept growing past budget, with no clear explanation why.

Pharma Logistics’ had a complex multi-warehouse data landscape with legacy ODBC-based integration jobs left over from the previous architecture pulling data inefficiently. Snowflake was underutilized in the wrong places and overprovisioned in others. Performance issues were impacting the analytics team. Without a governance layer, there was no reliable way to audit what was running, how often, or at what cost. The team needed answers fast, and a fix that wouldn't break the pipelines feeding their analytics teams.

Needing Fast Answers, Even Faster Results

Pharma Logistics’ VP of IT and Senior Manager of Data knew they needed to move fast. Every day of inaction was costly. They needed a partner who could jump in, understand the architecture quickly, diagnose the issues, and deliver results before the next billing cycle.

Pharma Logistics came to Turgon through DynPro, a strategic partner with an existing relationship at the company and strong track record in enterprise IT services. Turgon's AI-native delivery model combines specialized AI agents with human expertise, making it uniquely suited to compress what traditionally takes months of diagnostic and remediation work into weeks while delivering meaningful cost relief.

The Solution: Gain Visibility with AI Agents, Optimize with Business Context

Phase 1: 25% Cost Reduction in 6 Weeks

The hardest part of optimizing a data environment isn't the technical work. It's knowing what the data is actually supposed to be doing. Without that business context, you can right-size a warehouse and still be solving the wrong problem. 

Typically, Turgon’s pod of AI agents start by building the data ontology, integration logic, and orchestration layers in its first few days on the project. The agents have context, and can determine whether the underlying data movement is justified, redundant, or ripe for re-architecture.

Before making any optimization decisions, Turgon’s Snowflake Optimization Agent pulls business context for how data is being used, which workflows it's enabling, and what decisions are being made from it. Only then does it identify what's unnecessary, what's oversized, and what can be re-architected.

For Pharma Logistics, Turgon’s agent discovered an analytics dashboard querying Snowflake every five seconds and driving up costs. The team asked a simple question: what decision is actually being made on a five-second boundary? The answer was none. Managers were actually checking the data every 15 minutes. Re-tuning the query interval from five seconds to 15 minutes cost nothing to implement and saved a material amount in compute credits. But it only became possible once the team understood the actual use case.

That same logic applied across the architecture: legacy ODBC-based integration jobs pulling data they no longer needed, warehouses sized for peak loads that arrived once in a blue moon, ETL pipelines misaligned with actual usage patterns. Operations Research principles drove every re-architecture decision. 

All of this work happened while Pharma Logistics' data pipelines kept running. No downtime. No disruption to the analytics teams depending on daily supply chain and compliance reports. At the end of six weeks, they saw a 25% reduction in Snowflake spend, a fully documented architecture, and built-in testing and security controls.

Phase 2: A Monitoring Agent Finding 15% in Savings 

After the initial six-week engagement wrapped, Turgon kept its Monitoring Agent running to see whether the environment had stabilized or whether new inefficiencies would surface. They did. 

The agent identified additional optimization opportunities that hadn't been visible during Phase 1, delivering a further 15% reduction in Snowflake spend on top of the sprint savings. In total, Pharma Logistics saw a 40% reduction and a fully governed, documented architecture they could manage going forward.

40% Cost Reduction and a Foundation Built to Last

For a company built on regulatory precision and financial accountability, the 40% in savings is more than a cost win. Pharma Logistics now knows exactly what their data environment is doing, why it's doing it, and what it costs. That's not a one-time fix. That's a new operating standard.

Pharma Logistics didn't just have a Snowflake problem. They had a visibility problem. Turgon helped them understand why data exists and what it’s actually enabling, making the waste obvious, and paving a path toward more sustainable data and operations practices into the future. 

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