Intent Discoverer

Overview

The Intent Discoverer agent is designed to systematically analyze large volumes of raw customer conversation transcripts. Its primary function is to move beyond simple keyword matching by clustering these conversations into a structured, actionable taxonomy of user intents. The output is a machine-readable JSON file containing the defined intents, their relative volume rankings, and representative examples.

Capabilities

• Transcript Ingestion: Reads and processes conversation samples from specified directories (e.g., /data/bitext/).
• Intent Clustering: Groups disparate conversations into coherent, deployment-relevant intent categories.
• Volume Ranking: Ranks the discovered intents based on an approximation of their frequency or volume within the dataset.
• Structured Output Generation: Produces a final, validated JSON report containing all necessary metadata, including progress logs and communication summaries.
• Detail Capture: For each intent, it captures representative utterances and detailed long-tail notes for model training context.

Example Use Cases

1. NLU Model Training Data Preparation: Feed the agent thousands of raw chat logs to generate a clean taxonomy.json file ready for fine-tuning an NLU engine.
2. Service Gap Analysis: Run it against transcripts from underperforming service channels to discover unaddressed customer needs (long-tail intents).
3. Taxonomy Validation: Use the resulting structure as a baseline taxonomy to validate or expand upon existing intent models before deployment.