Modernizing Legacy Infrastructure to Real-Time Analytics in 5 weeks

About McCarthy Tire Service

McCarthy Tire Service is a 99-year-old commercial tire and wheel services company ranking among the top 5 commercial tire businesses in North America. They operate 100+ retail locations and a fleet of field technicians who service tires and perform on-site repairs for customers nationwide.

McCarthy runs its core inventory, sales, and purchasing operations on an IBM i AS/400 system installed more than 30 years ago, a platform run by 100,000 enterprises worldwide, but one that had grown disconnected from the real-time, cloud-based analytics the business needed to keep scaling.

The Challenge

McCarthy's IT team spent hours each week manually pulling sales and inventory data out of IBM i AS/400 system, cleaning and aggregating it across 100+ locations, then compiling the results into Excel reports for leadership. By the time those reports reached leadership the underlying data was already days old, so inventory and purchasing calls across a 100-location network were made on a lag instead of in real time.

Three decades of business logic lived across McCarthy's 40,000+ IBM i tables with almost no schema documentation, and the people who understood the system best were approaching retirement. Traditional systems integrators scoped a full discovery and modernization effort at $500,000 to $1.5 million and 6+ months, using consultant-led workshops to document what the system did before writing a line of migration code.

The Solution

Turgon deployed its pod of specialized AI agents to execute each step. The Data Dictionary Agent analyzed table structures and relationships across McCarthy's 40,000+ IBM i tables directly, examining actual data patterns to infer business logic the system had never documented and cross-referencing historical query logs to see how tables were used together. The agent surfaced foreign key patterns hidden in data values rather than schema definitions, and hierarchies embedded in decades of naming conventions, flagging ambiguous relationships for review. Turgon's data engineers then validated the resulting semantic model against McCarthy's actual business processes, resolving the handful of relationships with more than one plausible interpretation, and delivered a complete, verified semantic model in four days.

With the semantic foundation in place, Turgon's Data Integration Agent designed extraction logic to pull data from IBM i into Amazon S3 in Apache Iceberg format, respecting the legacy system's performance limits and connection constraints so production kept running untouched. Medallion Coding Agents then built a bronze/silver/gold pipeline in Snowflake: raw IBM i data landed in bronze, cleaning and standardization produced silver, and business logic aggregations built analytics-ready gold datasets, while Snowflake-specialist agents tuned warehouse configuration and clustering for McCarthy's query patterns.

Turgon built interactive AWS QuickSight dashboards on top of the Snowflake warehouse, giving leadership visibility into inventory across all 100+ locations, sales performance by region and product line, and purchasing patterns, refreshing automatically instead of on a weekly Excel cycle. Turgon also deployed a Data Dictionary Chat interface so McCarthy's business users can ask questions about table relationships, field definitions, and data lineage in plain language, without writing SQL.

Turgon Solutions

• Data Ontology & Lineage
• Data Migration & Pipeline Build 
• Data Warehouse Build (Snowflake) 
• Real-Time Analytics Integration (AWS QuickSight)
• Semantic Data Interface

‍The Results

Witnessing Turgon’s speed and exceptional quality of work, McCarthy Tire Service’s CIO Lee Lispi is a true believer of the power of AI-led infrastructure modernization and sees future opportunities to enhance their operations throughout his organization.

Documented legacy system

• McCarthy has a complete, verified map of every table, field, and relationship in the IBM i system, stored as a searchable data catalog, instead of relying on the tribal knowledge of a few employees.

Real-time visibility

• Leadership now sees inventory and sales across its 100+ locations, and can analyze performance by region and product line in dashboards that automatically refresh, replacing their previous manual Excel reports.

3x faster value delivery

• Turgon mapped 40,000+ tables and built the full semantic model in four days, then delivered production pipelines and live dashboards in five weeks total, that traditional integrators projected would take 6+ months.

IT spend cut by 70%

• Turgon delivered the engagement for 70% less than the $500,000 to $1.5 million other SIs quoted, using AI agents that handled 90% of the work plus 3 engineers, instead of the 8+ consultants other SIs bill hourly.

Governed foundation for AI 

• The semantic model and unified data foundation in Snowflake makes it possible to add new analytical capabilities, AI apps, and agentic workflows without delays from repeating discovery work.