You can't act 
on what you can't see

In one word: video.

Traditional social listening tools analyze text around the video: descriptions, captions, comments, hashtags. 

dig analyzes what’s inside the video: visuals, speech, tone, scenes, objects, emotions, and narrative structure.

With dig, you can tell whether something is sarcasm or praise, whether the product is actually shown on screen or merely mentioned in text, and whether sentiment is positive or negative inside the video itself.

dig’s in-video analysis runs at 95% accuracy across speech, visuals, emotion, and narrative classification.

That accuracy is measured against labeled datasets and continuously improved with human-in-the-loop evaluation. 

Every insight is traceable to the original video, so teams can verify exactly what the digger (the dig engine) detected.

dig analyzes 90%+ of social video across major platforms, including TikTok, YouTube, Instagram, Reddit, X, and Facebook.

This is not sampling. It’s continuous, large-scale in-depth analysis of billions of posts in real time.

LLMs summarize language, but they can’t do the same for video content. They don’t see visuals and can’t catch gestures, edits, or on-screen context

They also can’t interpret irony, reaction formats, or visual storytelling - and they sometimes hallucinate patterns without access to underlying video data.

dig is not a text model guessing about video.

It analyzes the video itself and produces insights grounded in actual footage:  clickable, reviewable, and defensible - so you can make high-stakes decisions.

The short answer is MINUTES.

Once you start, you can search in natural language, explore live narratives and run deep research flows in the dig chatbot and get your answers in minutes.

Most teams are up and running in under three (3) hours. No Boolean query setup, no taxonomy building and no manual tagging needed.

• Brand & consumer insights teams - for culture, perception, creative direction, and trend analysis.
  
• CMOs and strategy leaders - for positioning, risk, and studying the market.
  
• Comms & PR teams - for crisis detection, reputation, misinformation, and narrative tracking.
  
• Agencies  - for client pitch, influencer vetting, and full campaign analysis.
  
• Research and analytics teams - for qualitative market and consumer research at scale, without manual coding.
• Public sector and institutional teams - for public sentiment tracking, disinformation, threat detection, policy narratives, and trust monitoring across social platforms.

This isn’t faster research. It is social intelligence.

Manual video research:

• Is slow (takes weeks to months)
• Covers a fraction of what’s actually happening
• Biased by what analysts chose to watch
• Can’t be reproduced or audited at scale

dig:

• Analyzes billions of videos in real time
• Surfaces patterns that can’t be tracked manually
• Produces traceable, source-linked insights
• Eliminates the human bottleneck

For any narrative, trend, or claim, you just click through to the exact posts behind it. You can see which creators drove it, understand how it spread and verify the context - in minutes.

dig detects:

• Deepfakes and manipulated content
• Counterfeit products and brand misuse
• Misinformation and harmful narratives
• Off-brand or risky creators / influencers
• Emerging negative issues before they scale

dig analyzes what is shown and said inside the video, not just the caption - and that’s why it can detect threats that text-based systems fail to catch.

Most teams understand what they’re missing when they research their own category, brand, or audience with dig.

In a demo, you’ll see for yourself:

• How narratives actually appear inside video
• How insights link directly to real social videos
• How quickly you can go from question → evidence → to decision

If your decisions depend on data - watching in-video intelligence form in real time is a game changer.