• THEMES
  • MAPPING
  • SUMMARY
  • HUMAN

How to extract conversation topics with your AI

Connect Deep Talk to your AI client once. From there, your agent can run automated NLP pipelines to find patterns in your text.

Ask AI about this page

Short answer

How can I pull themes from my text data using AI?

Your agent runs NLP pipelines that scan your raw text and group similar ideas into clusters. You get a structured map of the main subjects in your data.

Thematic outputs

Where your data lands.

The AI processes your text into these specific results.

THEMES

Identified topic clusters

The AI groups related snippets into coherent themes. You get a clear view of the main subjects present in your dataset.

MAPPING

Raw text connections

Every theme links back to the original text. You can see exactly which parts of your data triggered each topic.

SUMMARY

High level overviews

The AI condenses large volumes of text into brief descriptions of each cluster. This makes scanning massive datasets much faster.

HUMAN

Manual review needed

The AI identifies the patterns, but you decide which themes are actually useful. You provide the final context and judgment.

The workflow

What your AI does when the data arrives.

The AI handles the heavy lifting of scanning and grouping text through structured pipelines.

  1. Find pipelines

    Your agent checks which NLP processing methods are available for your specific dataset.

    list_pipelines

  2. Check pipeline details

    The AI inspects the configuration of a pipeline to ensure it matches your goal for topic extraction.

    get_pipeline_details

  3. Start processing

    The AI begins the analysis by sending your text through the selected NLP pipeline.

    create_batch_prediction

  4. Get results

    Once the processing finishes, the AI pulls the final clusters and themes for you to review.

    get_batch_prediction

Try it

Copy these to start.

Use these prompts to kick off the extraction process.

Starting points

These are opening lines. Swap the bracketed text for your actual file names or data sources.

Deep Talk Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_p1r9lyvKUygLDYkDHtS0iQrZChD9ZM45aBPc7pHA/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Deep Talk capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "deep-talk-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_p1r9lyvKUygLDYkDHtS0iQrZChD9ZM45aBPc7pHA/mcp"
    }
  }
}
Copy into chat04

  • List the available NLP pipelines I can use with my customer feedback sheet.

  • Run a topic extraction pipeline on the transcript file from the sales call.

  • Check the status of the batch prediction I just started on the support logs.

  • Show me the identified themes from the latest analysis of the community forum export.

  • Claude
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  • Cursor
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  • JetBrains
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Start here

Connect Deep Talk once, then ask.

One link connects your agent. Your login stays encrypted on our side, and you can start working immediately.

Connect Deep Talk to your AI

FAQ

How this task behaves

  • 01

    Can the AI change my original text files?

    No. The AI only reads your data to identify patterns; it does not modify or overwrite your source files.

  • 02

    Does the AI decide which topics are important?

    The AI identifies mathematical clusters in the text. You are the one who decides which of those themes matter for your work.

  • 03

    Can I create my own custom NLP pipelines?

    No. You can only select and run from the pipelines that are currently available in the system.

  • 04

    Will the AI delete any of my data?

    No. This connector is designed for read-only analysis and extraction.

  • 05

    How does it handle very large datasets?

    It uses batch prediction to process large amounts of text in chunks rather than trying to read everything at once.

  • More questions about Deep Talk? The Connector page answers them. See everything the Deep Talk Connector can do

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