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Artem Frolov

Case study · Applied AI / Operations

Customer Call Transcript Analysis

A daily Databricks workflow that turns customer call transcripts into structured data for teams to investigate recurring issues and sentiment.

By
Artem Frolov
Stage
In operational use
Tools
Databricks, Unity Catalog, Python, OpenAI Batch API, LLM structured extraction, SQL tables, Databricks Apps
Also in
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The problem

Customer calls contain useful evidence about recurring issues, sentiment, and process failures. That evidence is hard to compare while it remains in long transcripts. Operations teams need structured data they can query and explore without reading each conversation.

From conversation to queryable data

  1. Ingest: collect customer call transcripts in Databricks, with access governed through Unity Catalog.
  2. Extract: run structured LLM analysis in a daily batch to identify issues, sentiment and operational signals.
  3. Explore: write results to SQL tables and make them available through a Databricks app for Operations.

The approach

  • Stored transcripts in Databricks with access governed through Unity Catalog.
  • Built a daily batch pipeline to ingest and analyse transcripts.
  • Used structured LLM outputs to classify calls, assess sentiment, and extract operational signals.
  • Converted the results into SQL tables for analysis and reporting.
  • Built a Databricks app for Operations teams to explore trends and customer issues.

What changed

  • Moved the workflow into regular operational use with daily transcript processing.
  • Made customer issues, sentiment, and process signals available as structured, queryable data.
  • Reduced reliance on manual transcript sampling when investigating customer conversations.

Tools & technology

  • Databricks
  • Unity Catalog
  • Python
  • OpenAI Batch API
  • LLM structured extraction
  • SQL tables
  • Databricks Apps