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Particle’ Podcast Intelligence indexes 100,000+ shows, fully transcribed, diarized, speaker-identified, labeled, and enriched with metadata within minutes of airing, making spoken conversations searchable. Each result is a speaker-attributed transcript window with timestamps.

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Use it for

  • Finding expert commentary and quotable soundbites.
  • Media and brand monitoring.
  • Narrative and sentiment research.
  • Discovering and staying up to date with podcasts.

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Provider ID

Use this value in dataSources: 
particle

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Example

Find what podcast hosts are saying about AI regulation. 
from exa_py import Exa

exa = Exa()
run = exa.agent.runs.create(
    query="What are prominent podcast hosts and guests saying about AI regulation in 2025?",
    data_sources=[{"provider": "particle"}],
    output_schema={
        "type": "object",
        "required": ["mentions"],
        "properties": {
            "mentions": {
                "type": "array",
                "maxItems": 10,
                "items": {
                    "type": "object",
                    "required": ["podcast", "episode", "speaker", "quote", "stance"],
                    "properties": {
                        "podcast": {"type": "string"},
                        "episode": {"type": "string"},
                        "speaker": {"type": "string"},
                        "quote": {"type": "string"},
                        "stance": {"type": "string", "description": "pro-regulation, anti-regulation, or nuanced"},
                    },
                },
            }
        },
    },
)
run = exa.agent.runs.poll_until_finished(run.id)

import Exa from "exa-js";

const exa = new Exa();
const run = await exa.agent.runs.create({
  query: "What are prominent podcast hosts and guests saying about AI regulation in 2025?",
  dataSources: [{ provider: "particle" }],
  outputSchema: {
    type: "object",
    required: ["mentions"],
    properties: {
      mentions: {
        type: "array",
        maxItems: 10,
        items: {
          type: "object",
          required: ["podcast", "episode", "speaker", "quote", "stance"],
          properties: {
            podcast: { type: "string" },
            episode: { type: "string" },
            speaker: { type: "string" },
            quote: { type: "string" },
            stance: { type: "string", description: "pro-regulation, anti-regulation, or nuanced" },
          },
        },
      },
    },
  },
});

curl -s -X POST "https://api.exa.ai/agent/runs" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $EXA_API_KEY" \
  -d '{
    "query": "What are prominent podcast hosts and guests saying about AI regulation in 2025?",
    "dataSources": [{ "provider": "particle" }],
    "outputSchema": {
      "type": "object",
      "required": ["mentions"],
      "properties": {
        "mentions": {
          "type": "array",
          "maxItems": 10,
          "items": {
            "type": "object",
            "required": ["podcast", "episode", "speaker", "quote", "stance"],
            "properties": {
              "podcast": { "type": "string" },
              "episode": { "type": "string" },
              "speaker": { "type": "string" },
              "quote": { "type": "string" },
              "stance": { "type": "string", "description": "pro-regulation, anti-regulation, or nuanced" }
            }
          }
        }
      }
    }
  }'

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Pairs well with

  • Financial Datasets: cross-check podcast chatter against published news.
  • Fiber.ai: attach company and contact context to the people being discussed.
Last modified on July 4, 2026