The Enterprise Social Listening Platform Teams Trust
The digger
Three analytical layers. One clear picture. dig into the intelligence stack behind everything you see on social.
In-Video Intelligence: See Inside Every Frame
While others rely on captions or metadata, diganalyzes the video itself. See what’sactually happening inside the video. Analyze people's appearance, product placement, logos, and scenes -frame by frame with 95% tagging accuracy across 102 languages.
Context at scale
Understand the meaning, not just mentions. dig's social media intelligence goes beyond sentiment. Capture themes, discourse, and nuance exactly as people express them. No keyword guessing. No Boolean workarounds. Just context, at scale.
Detect Fake Engagement and Measure Real Influence
Not all reach is equal. dig shows you which signals you can trust. Separate real influence and genuine feedback from artificial noise. Detect fake engagement, identify coordinated amplification, and protect your brand's online reputation without the overload of manual monitoring.
The only deep research chatbot built for a video-first world
Get the evidence you need to move faster. dig transforms massive video datasets into strategic clarity, through a single, seamless conversation.
Run deep social research in one conversation
Ask complex and strategic questions and source authentic answers from social in one uninterrupted, conversational research flow.
Insights traced back to the source
Every narrative, claim, and conclusion is backed by visible post-level sources. With 100% traceability to original content, you always know exactly where each insight comes from.
Research-grade outputs built for action
Get structured narratives, clear summaries, and research-grade conclusions that teams can confidently share, defend, and act on.
The dig Intelligence Engine: Always-On Brand Monitoring
A strategic platform engineered to replace noisy, text-based data with high-relevance, video-first social media intelligence. Trusted by P&G, Starbucks, and Mastercard, dig gives enterprise teams the evidence and confidence they need to move from discovery to decision with total transparency.
Detect Fake Engagement and Measure Real Influence
Analyze creators beyond surface-level metrics understand narratives, behavior patterns, credibility, and real influence over time.
Social Video Monitoring Live Feed
Watch relevant social video activity unfold as it happens across platforms, topics, and creators -filtered, contextualized, and ready for close examination.
Sources
Full traceability for every insight, with direct access to the original creators and the videos behind each conclusion.
Deep Social Video Research
Run multi-step, end-to-end social research across massive video datasets, without breaking context or restarting queries.
Faster Social Analysis
Structured, pre-set starting points that help you launch focused research flows and move quickly into deeper analysis.
Social Intelligence Dashboard
An integrated view of narratives, trends, risks, and performance, built specifically for high-stake decision-making.
Collections
Organize videos, creators, and insights into structured research spaces you can revisit, share, and build on.
Smart alerts
Get notified in real time when narratives shift, risks emerge, or key topics spike. dig's real-time social listening alerts mean your team never finds out about a brand crisis after the fact.
Got questions?
We’ve got answers.
dig Enterprise is a social and video intelligence platform that reads what is said, shown, and felt inside social content, then tells brand, comms, and risk teams what story is forming, who is driving it, whether it is authentic, and what to do next.
It goes beyond counting mentions. dig analyzes video, image, audio, and text together across TikTok, YouTube, Reels, Shorts, Instagram, X, Reddit, and Facebook, connects related posts into the narratives forming around a brand, and routes every finding into a clear action path through the RESPOND framework: monitor, counter, promote, or take down. Every insight links back to the exact source post, so conclusions are auditable rather than black-box. dig Enterprise is built for communications, risk, legal, insights, and public-sector teams that need defensible, source-traceable intelligence.
Most social listening tools were built for a text-first internet. They monitor what is written about a brand and read video, at best, as a transcript. dig was built for a video-first social landscape and sees inside the video itself.
Three differences set dig apart. First, video-native understanding: dig reads speech, on-screen text, visual context, objects, tone, and sentiment frame by frame, not just the caption or a transcript. Second, narrative intelligence: instead of counting how often a brand is named, dig connects related posts into storylines and tracks how each gains momentum across creators and platforms, so teams catch a narrative while it is still early enough to shape. Third, action: most tools deliver an alert and stop, while dig produces a recommended response path with impact scoring, so teams spend time on what is likely to escalate rather than every spike of noise.
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 videos 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.