Trusted by Research Teams
Traditional research
wasn’t built for video
Manual research doesn’t scale
Manually reviewing video content isn't scalable. It's slow, inconsistent, and impossible to maintain across platforms and formats.
Legacy tools miss sentiment
Text-based tools ignore tone, visuals, emotion, and context, missing the signals that reveal how real users actually behave on video.
The “why” is a black box
You can catch the trend, but without traceability to source, root causes, and timing remain a mystery. Research teams need evidence, not summaries.
Rapid cultural shifts
Video culture moves faster than any manual research workflow can track. By the time insights reach a report, it’s often too late.
Fragmented platform visibility
Narratives, emotions, and gestures are coming in simultaneously across TikTok, YouTube, and Instagram - yet legacy tools still monitor one at a time.
Enterprise-grade capabilities, built for a video-first world
dig gives research teams strategic value, delivering in-depth insights in half the time and a fraction of the effort.
Automated qualitative research
dig analyzes speech, visuals, scenes, objects, captions, and emotion across billions of videos, delivering fast, consistent insights without manual tagging or bias, giving research teams findings they can stand behind.
Discovery tool for in-depth analysis
AI-powered clustering that goes beyond surface trends to uncover hidden patterns, mood shifts, and cultural changes before they start spreading.
Research in plain language
Ask complex research questions in natural language and instantly receive structured, traceable insights. Research teams get answers without waiting for a full analysis cycle.
See user behavior across platforms
Map how tone, sentiment, and behavior shift across TikTok, YouTube, Instagram, and more. Get insights on both the platform and category level.
Turn insights into decision-ready outputs
Generate outputs such as automated summaries, transcripts, and weekly intelligence briefings built for immediate action. Everything your research team needs to present consumer insights with confidence.
Analysis you can defend
Combine quantitative scale with qualitative depth, giving you consumer intelligence your research team can explain, validate, and stand behind.
Real-time video research finally at scale
Understand culture more clearly as it unfolds across billions of videos in real time. Track narratives, analyze behavior, and uncover trends without manual coding.
90%+
Coverage of all social video
See cultural signals most research tools never capture.
95%
Accuracy in in-video analysis
Reliable in-video analysis of speech, visuals, emotion, and context.
Billions of videos analyzed in real time
From niche communities to global-scale trends.
Real-time narrative detection
Spot trends as they emerge, before they gain momentum.
Automated video-first reporting
No manual coding, no bottlenecks.
Got questions?
We’ve got answers.
dig has three plans. Free is $0 a month with 10 research credits. Pro is $25 a month with 50 credits and instant top-ups. Enterprise is custom-priced with unlimited questions, built for always-on intelligence at scale. One credit equals one question answered by the dig chatbot.
For research teams, the practical split is simple. Free and Pro run on the ask-dig chatbot and are ideal for individual analysts doing social research, trend discovery, and competitive work. Enterprise adds unlimited questions, wider coverage across video, visual, text, online news, and publisher sites, real-time alerts, narrative intelligence, and enterprise governance. Reports export to PDF or CSV on every tier. For team-level scope and Enterprise pricing, book a demo.
dig analyzes more than 750 million social posts every month, with 90%+ coverage of social video across TikTok, YouTube and Shorts, Instagram and Reels, Facebook, Reddit, and X, plus fringe platforms where narratives often start before they go viral. Enterprise coverage also spans online news and publisher sites.
Unlike text-first tools that read captions and metadata, dig analyzes the content inside the video itself: speech, on-screen text, visuals, scenes, objects, and emotion, across more than 100 languages. dig works from publicly available social and web content, and every insight traces back to the original post, so research teams can see exactly which video each conclusion came from.
dig fits into research workflows through exports and enterprise access controls. Narrative summaries, transcripts, and impact scores export to PDF or CSV on every plan, so findings drop straight into your decks, reports, and existing analysis tools. Enterprise adds SAML SSO for access inside your identity stack.
Because dig runs on natural-language questions rather than Boolean queries or a fixed taxonomy, most teams use it alongside their current stack without a heavy setup or migration. Enterprise deployments run in a dedicated organization environment with audit logs and full data isolation.
Minutes. On the ask-dig chatbot you can go from sign-up to your first deep insight in under three minutes, because you ask questions in plain language instead of building Boolean strings. There is no taxonomy to design and no manual video coding.
For Enterprise, most teams are fully up and running in under three hours. Once you start, you can search in natural language, explore live narratives, and run multi-step deep research flows, then get structured, source-traceable answers in minutes rather than weeks.
On the Enterprise plan, your data is never used to train dig's models. Enterprise runs in a dedicated organization environment with full data isolation, SAML SSO, and audit logs, so your queries and the intelligence lens you build stay your own.
dig processes publicly available social and web content to generate insights, and operates under GDPR and UK GDPR, with appointed Article 27 representatives in the EU and UK and a standing process for data-subject rights (access, deletion, portability, and more) via privacy@dig.ai. Note that on the Free and Pro plans, usage data may be used to improve and train the underlying models, while Enterprise data is excluded from training entirely. International transfers are handled under applicable legal safeguards.