How Narwal Built a Snowflake AI Assistant to Transform Transportation Analytics into Self-Serve Logistics Insights

Summary

A global manufacturer relied on data analysts to access critical transportation data stored in Snowflake, creating a 3-to-5-day delay for operational insights. While traditional BI dashboards provided high-level, predefined KPIs, they could not support the fluid, ad hoc questions logistic managers asked daily.

To bridge this gap, Narwal built a Snowflake AI Assistant for transportation analytics using Cortex AI and enterprise semantic modeling. Logistics teams can now query shipment performance, delay patterns, and cost drivers directly through natural language. Questions that once took days to resolve are now answered via self-service in less than 30 seconds, while keeping all data securely inside Snowflake.

Customer Challenge

The manufacturer generated massive amounts of transportation data from its global freight operations. While shipment records, carrier performance metrics, and freight costs were organized inside Snowflake, they were functionally locked behind a technical gate. Because logistics managers lacked SQL expertise, every routine operational question required a data ticket, introducing severe operational friction.

• Delayed Decision-Making: Answers often arrived too late to support timely supply chain adjustments.

• Misallocated Technical Resources: Core data analysts spent a significant portion of their time fielding repetitive, routine data requests instead of focusing on high-impact strategic data engineering.

• Rigid Dashboard Limitations: Existing static reports could not answer complex, cross-domain questions on the fly, forcing teams to wait on technical overrides to get granular data.

The questions the logistics and supply chain team needed answered daily had no fast path to answers:

• Which carriers had the highest delay rates across our lanes this month?
• Show me the freight lanes experiencing the largest cost variance compared to last quarter.
• How have delivery times shifted across our major distribution points over the last 90 days?

Narwal’s Solution

Narwal designed a unified, Snowflake-native architecture that translates conversational business questions into precise, secure SQL queries in real time, built around core business capabilities.

Self-Service Supply Chain Analytics

Logistics and operations teams can now ask questions in plain language and receive immediate, data-grounded answers. This capability is powered by Cortex Analyst and orchestrated securely inside the client’s governed Snowflake environment, ensuring sensitive transportation and operational data never leaves the platform.

Trusted Enterprise Data Accuracy

Consistency was achieved through a transportation-specific Semantic View that standardized shipment, carrier, lane, delay, and cost metrics into a single business layer. This layer ensures the assistant interprets complex business queries reliably, aligning the data model with how the business actually measures performance.

Deep Multi-Table & Cross-Domain Reasoning

Transportation analytics rarely lives in a single silo. The semantic model was built to unify operational and planning datasets, enabling business users to explore relationships across transportation performance, delays, and cost trends without understanding the underlying data structure.

Context-Aware Operational Deep Dives

Powered by a Snowflake Agent, the assistant maintains context across multi-turn conversations. Users do not need to restate their parameters with every query; they can ask follow-up questions, drill down into a specific carrier, or refine a line of inquiry seamlessly.

Business Outcomes

• Faster Access to Operational Insights: Questions regarding shipment performance, lane trends, and cost drivers that previously required up to five business days of analyst turnaround are now resolved by the Snowflake AI Assistant in under 30 seconds.
• Significant Drop in Ad Hoc Tickets: Empowering logistics and operations teams to access insights independently cut the volume of routine data requests hitting the technical team by an estimated 40% to 50%.
• Reclaimed Analyst Capacity: Meaningful technical capacity was successfully redirected away from repetitive reporting tasks, freeing analysts to focus on advanced data initiatives that require their specialized expertise.
• Built for Growth: The solution is designed to scale as new transportation datasets, business processes, and operational domains are added to the Snowflake environment.

Why Narwal

The success of this solution depended on creating a semantic foundation capable of accurately interpreting transportation data across multiple operational domains. Narwal combined deep Snowflake AI expertise with advanced enterprise semantic modeling to deliver a governed, scalable solution that logistics teams can trust for critical daily decision-making.

Ready to Turn Transportation Data into Actionable Insights?

Don’t let critical supply chain intelligence remain locked behind technical bottlenecks. Partner with Narwal to create a governed, self-service analytics experience that helps operational teams make faster, data-driven decisions.