Posts by: narwal@
How Narwal Built a Snowflake AI Agent to Transform Product Reviews into Instant, Governed Insights
Background Product reviews tell a story, but for a global manufacturer managing 50+ product lines across multiple retail channels, that story was trapped; until an AI agent built natively on Snowflake made it accessible. Business…
- Jul 08
How Narwal Helped a Global Manufacturer Turn AI-Powered Product Sentiment into Enterprise-Wide Decision Intelligence
Summary A leading global manufacturer had already deployed a Snowflake-native AI product sentiment assistant to analyze product reviews and surface actionable insights, but access remained limited to technical teams. Product and business functions had no…
- Jul 08
Achieving 93% Consistency: How Narwal Built a Production-Ready AI Sentiment Assistant for a Global Manufacturer
Background A leading global manufacturer operating across a diverse portfolio of building and consumer products needed an AI sentiment assistant to make sense of large volumes of customer reviews across retail, distributor, and digital channels…
- Jul 08
How Narwal Used Snowflake Cortex AI to Transform Unstructured Safety Notes into Proactive Workforce Intelligence
Summary A leading American manufacturer relied on manual tracking and unstructured safety notes to monitor workplace safety, creating a 5-to-7-day lag in identifying critical behavioral trends and coaching opportunities. While traditional safety metrics captured lagging…
- Jul 02
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…
- Jun 29
5 Signs Your Data Engineering for AI Is Solving the Wrong Problems
Your data engineering team is doing the work. Pipelines are up and running. Tickets are getting closed. Yet data engineering for AI is a different game, and the gap is starting to show. AI initiatives…
- Jun 26
The Future of Quality Engineering: 5 Core Shifts Redefining QE for AI in 2026
Quality Engineering is no longer a downstream checkpoint. In 2026, it sits at the center of business confidence. The latest piece on NASSCOM breaks down five shifts redefining QE this year: the rise of agentic…
- Jun 22
AI Agent Orchestration: Building the Foundation for Enterprise AI Automation
Unlike standalone AI agents that perform isolated tasks, AI agent orchestration enables intelligent collaboration, context sharing, decision-making, and workflow execution across complex enterprise environments. As enterprises invest in enterprise AI automation, agentic orchestration is emerging…
- Jun 22
Enterprise Regression Testing Strategy: How to Modernize QE with AI and Risk-Based Testing
Every enterprise wants faster releases. The challenge is doing that without increasing risk. As applications become more complex and delivery cycles accelerate, regression testing is no longer just a quality checkpoint. It plays a critical…
- Jun 08
AI Maturity Assessment: The Missing Link Between AI Pilots and Enterprise Scale
Enterprises today are investing heavily in Artificial Intelligence (AI). From experimenting with generative AI to building proof-of-concept models, there is no shortage of ambition. Yet, despite this momentum, only a small fraction of organizations successfully…
- May 26
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