Snowflake
Case Study

Delivering Faster, Smarter Customer Insight Through Snowflake Cortex AISQL

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PROLIM leveraged Snowflake Cortex AISQL functions (AI_CLASSIFY, AI_SENTIMENT, AI_SUMMARIZE_AGG) to transform a client’s unstructured customer feedback—spanning millions of support tickets, social media posts, and product reviews—into actionable insights. The solution eliminated the need for external ML infrastructure and specialized data science teams, reducing time-to-insight and empowering business units such as Customer Support and Product Management to proactively manage churn and enhance product quality.

  • Slow Time-to-Insight: Processing and analyzing large volumes of unstructured feedback caused insights to lag weeks behind real-time events.
  • Data Silos and Movement: Unstructured data resided outside Snowflake, requiring complex ETL/ELT workflows and introducing security and governance risks.
  • High Barrier to AI Adoption: Text analytics relied on Python expertise and dedicated ML infrastructure, making AI inaccessible for SQL analysts.

  • Native LLM Functions: Ingested customer feedback directly into Snowflake tables. The AI_SENTIMENT function analyzed tone (positive, negative, neutral) for each record.
  • Custom Classification: The AI_CLASSIFY function categorized complaints into business-specific topics such as Shipping Delay, Faulty Component, and Billing Issue.
  • Automated Summarization: The AI_SUMMARIZE_AGG function generated daily executive summaries highlighting root causes of negative feedback.
  • Zero Infrastructure: The serverless Cortex environment removed the need for external GPU clusters or ML services.

  • Time-to-Insight reduced from weeks to minutes, enabling real-time crisis detection and response.
  • Elimination of costs associated with external cloud-based ML/GPU infrastructure and reduced reliance on highly specialized data scientists.
  • Democratized AI by allowing standard SQL analysts to execute complex AI tasks, accelerating user adoption across departments.
  • Data never left the secure Snowflake boundary, ensuring compliance with strict data privacy and security mandates.

  • 90% reduction in latency for sentiment analysis and feedback processing.
  • 30% faster detection of emerging product defects and service issues.
  • 15% improvement in first-call resolution due to categorized historical data.
  • Deployed three new AI pipelines without Python code or additional infrastructure.

Snowflake Streamlit provided a unified, secure, and scalable environment for interactive data storytelling. Through PROLIM’s solution, the client gained a self-service analytics platform within Snowflake—bridging the gap between data engineering and business decision-making, and significantly enhancing operational agility.

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