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Working Case Study

Conversation Analytics

AI Transcript Intelligence

An analytics system that turns customer calls, reviews, and transcripts into structured insight — intent, sentiment, issues, and escalation signals.

  • Python
  • LLM Classification
  • Structured Output
  • Sentiment Analysis
  • Summarisation

01

Problem

Support calls, sales conversations, and review streams contain the most direct customer signal a company has — and almost none of it is read. The volume makes manual analysis impossible; generic summarisation loses the categories teams actually act on.

02

Solution

The pipeline runs each transcript through staged LLM analysis with structured output schemas, producing fields that map to decisions: what the customer wanted, how it went, what should happen next, and who needs to know.

  • Intent and topic classification
  • Sentiment and emotional trajectory
  • Issue extraction and recurrence grouping
  • Churn-risk and escalation signals
  • Per-audience summaries: product, support, leadership

03

What the system proves

That conversation analytics is a structured-extraction problem, not a chatbot problem. The same pattern extends to call-center QA, review mining for e-commerce, and voice-of-customer reporting — domains where the input is messy language and the output must be a table someone can act on.

What makes it credible

  • Multi-view output: product, support, sentiment, churn-risk, leadership
  • Structured JSON insights instead of freeform summaries
  • Public repo with the full pipeline
  • Clearly scoped, with a documented improvement roadmap

Best fit for

  • Customer support & product teams
  • Founders analysing calls, reviews & feedback
  • Teams needing intent, sentiment & action points

Operating principles applied

  • Structured extraction before dashboards
  • Decision-ready fields over freeform summaries
  • Scope kept honest and documented

Roadmap

  • Multi-file and batch ingestion from call-platform exports
  • Dashboard metrics with role-based views
  • Topic clustering across conversations
  • Exportable reports and stronger schema validation
  • Evaluation set with labelled transcripts

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