Social intelligence platform dig has launched ask-dig, a video-first AI search system designed to surface real-time, evidence-backed insights from social media conversations. Positioned as an alternative to general-purpose AI search tools, the platform analyzes short-form videos, posts, and comments to extract authentic audience sentiment and emerging narratives—addressing a growing gap in how traditional LLMs interpret social discourse.

The rise of short-form video has fundamentally changed how narratives form and spread across platforms like TikTok, Instagram, and YouTube Shorts. Yet most AI systems and search tools continue to rely heavily on text-based indexing, often missing the emotional nuance, context, and immediacy embedded in video-driven conversations.

dig’s latest product, ask-dig, is designed to bridge that divide.

Unlike conventional large language model (LLM) search tools that generate responses from broad web corpora, ask-dig functions as a real-time social intelligence engine. It retrieves and analyzes thousands of social media posts and videos in under two minutes, focusing specifically on unfiltered user-generated content rather than curated or syndicated sources.

The platform’s core differentiator lies in its video-centric analysis layer. ask-dig evaluates social content not just through captions or metadata, but by examining narratives, comment sentiment, audience reactions, and emerging thematic signals embedded within short-form video ecosystems. This approach reflects a broader shift in digital behavior where video has become the dominant medium for opinion formation and cultural discourse.

Once a user enters a natural language query, ask-dig compiles relevant social posts, filters out sponsored or synthetic content, and constructs a response grounded entirely in verified social sources. Each output includes direct links back to original posts, enabling users to trace insights back to their source material—an increasingly important requirement in the era of AI-generated summaries and synthetic content proliferation.

“Most AI platforms today produce answers that sound authoritative, but lack grounding in real-world sentiment and behavior,” said Ofer Familier, CEO and Co-founder of dig. “The problem is that opinions are forming in conversations taking place through dynamic interactions across social, increasingly in videos.”

His statement highlights a key limitation in current AI search paradigms: the flattening of multimodal, emotionally rich content into text-only representations. As a result, many AI-generated insights fail to capture the behavioral signals that define brand perception in real time.

ask-dig attempts to correct this by preserving the structure of social context. Instead of treating posts as isolated data points, it analyzes them as part of evolving narrative clusters, identifying how sentiment shifts across creators, communities, and formats.

The use cases extend across multiple professional domains. Journalists can use the platform to track real-time reactions to breaking news stories. Marketers and brand teams can analyze campaign sentiment as it unfolds. Creators and consultants can identify emerging trends before they reach mainstream visibility. Even consumer-facing queries—such as product recommendations or location-based insights—can be grounded in live social discourse rather than static web content.

This positions ask-dig within a fast-emerging category of AI-native social search engines. Unlike traditional social listening tools that rely on keyword tracking and dashboards, ask-dig functions as an interactive query engine that delivers synthesized answers in natural language.

The distinction is important in a broader industry context. Legacy social analytics platforms were built for monitoring mentions and volume-based metrics. However, the modern social ecosystem is increasingly shaped by video-first platforms where meaning is conveyed through tone, visual context, and community interaction rather than text alone.

By focusing on video-native intelligence, dig is aligning with a structural shift in digital communication. According to industry research from McKinsey, video content is now the fastest-growing format in digital media consumption, accounting for a significant share of user engagement time across major platforms. At the same time, Gartner has noted that enterprises are increasingly prioritizing real-time sentiment analysis as part of broader brand intelligence strategies.

ask-dig is not positioned as a replacement for enterprise monitoring tools but as a complementary layer. It is part of dig’s broader platform ecosystem, which includes always-on social intelligence systems designed for continuous brand tracking, narrative detection, and consumer insight generation.

While the enterprise platform focuses on long-term monitoring and structured analytics, ask-dig emphasizes immediacy—turning social media into a queryable intelligence layer that responds in near real time.

This dual approach reflects a broader trend in AI product design: the separation of continuous intelligence systems from ad-hoc retrieval engines. One handles ongoing signal detection; the other provides instant, conversational access to those signals.

As AI search evolves, platforms like ask-dig are also raising questions about authenticity and data provenance. By explicitly sourcing responses from verifiable social content and linking outputs back to original posts, dig is attempting to address growing concerns around hallucination, synthetic summaries, and unverifiable AI-generated claims.

Market Landscape

The emergence of video-first AI search reflects a larger transformation in how information is created, distributed, and consumed across social platforms. As short-form video becomes the dominant communication format, traditional text-based search engines and LLMs are increasingly limited in their ability to capture context-rich narratives.

Social intelligence platforms are evolving rapidly in response. Legacy tools built around keyword tracking and engagement metrics are being replaced by systems that incorporate multimodal AI, sentiment analysis, and real-time data ingestion from video-heavy ecosystems.

At the same time, enterprises are under pressure to understand not just what is being said about their brands, but how it is being expressed visually and emotionally. This has created demand for AI systems capable of interpreting tone, context, and behavioral signals at scale.

ask-dig enters this environment as part of a new generation of AI-native social search platforms that treat social media as a live intelligence feed rather than a static dataset.

Top Insights

• dig launched ask-dig, a video-first AI social search platform that analyzes short-form videos and posts to deliver real-time, evidence-backed insights from social media conversations.
• The platform differentiates itself from traditional LLM search by sourcing responses exclusively from verified social content and linking answers back to original posts for transparency.
• ask-dig enables use cases across journalism, marketing, consulting, and brand intelligence by surfacing emerging narratives and sentiment shifts in real time.
• Industry trends from McKinsey and Gartner highlight the rapid rise of video consumption and increasing enterprise demand for real-time sentiment and narrative intelligence tools.
• The launch reflects a broader shift toward AI-native social search systems that prioritize multimodal understanding over text-based indexing and keyword tracking.

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