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DeepDive helps teams collect, analyze, and act on large-scale online conversation data. It processes unstructured data from social media, communities, and review platforms, turning it into structured insights through NLP, signal analysis, and automated workflows. DeepDive is designed to integrate seamlessly with external tools and data systems. Through APIs and integrations, teams can enhance their workflows with real-time social signals, including brand mentions, sentiment, intent, emerging topics, and risk indicators.

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What DeepDive Provides

DeepDive enables users to:
  • Monitor conversations across multiple platforms and sources
  • Analyze sentiment, topics, narratives, and intent at scale
  • Detect early signals, anomalies, and potential risks
  • Export structured intelligence into downstream tools for activation

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Using DeepDive with Integrations

DeepDive supports integrations that enable social intelligence to flow seamlessly into operational systems, including CRMs, enrichment tools, analytics platforms, and automation pipelines. In this documentation, you’ll find:
  • How DeepDive data is structured and exposed
  • Step-by-step guides for integrating DeepDive with external tools and platforms
  • Examples for syncing signals, alerts, and enriched attributes into external workflows
This documentation focuses on how to connect, configure, and use DeepDive data programmatically, rather than on product features or marketing use cases.

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Supported Platforms

PlatformEndpointsStatus
TikTokPosts, Comments, Bulk Posts, Hashtag SearchAvailable
InstagramPosts, Comments, Hashtag SearchAvailable
YouTubeSearch, Comments, Hashtag Search, Channel Videos, Channel ShortsAvailable
RedditSubreddit Posts, Post CommentsAvailable
LinkedInCompany Posts, Profile DataComing Soon
Twitter (X)Tweets, User Timeline, SearchComing Soon
FacebookPage Posts, Group Posts, Feed Posts, CommentsComing Soon

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Key Features

Multi-Platform

Access TikTok, Instagram, YouTube, and Reddit through a single API

ML Enrichment

Analyze content with sentiment, topics, intent, and keyword extraction

Credit-Based Pricing

Transparent per-request credit consumption

Pagination

Efficiently handle large datasets with cursor-based pagination

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Response Format

All endpoints return a consistent response structure: 
{
  "success": true,
  "data": { ... },
  "metadata": {
    "credits_used": 15,
    "processing_time": 1.234,
    "enrichments_applied": ["sentiment", "topics"]
  }
}

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Next Steps

Getting Started

Make your first API call in minutes

Authentication

Learn about API key management

ML Enrichment

Explore AI-powered content analysis

Platform Guides

Detailed guides for each platform