dig Launches ask-dig, Video-Centric AI Social Search Platform

The social intelligence company dig announced the launch of ask-dig on April 16, 2026, positioning it as the first video-centric AI search platform that delivers answers grounded in real social media content. The product arrives at a moment when short-form video has become the dominant medium for opinion formation across platforms like TikTok, Instagram, and YouTube Shorts.

Traditional large language models still rely heavily on text-based sources and non-social media content to generate responses. These answers may sound confident, but they're often outdated, inauthentic, and miss the emotional responses of ordinary people that actually define brand perception. (This has been a problem for years, frankly.)

According to the official press release, ask-dig analyzes social posts and cuts through sponsored content and synthetic material to surface unfiltered audience reactions. The platform finds and examines thousands of relevant social posts in under two minutes, looking at narratives, comment sentiment, and emerging signals across feeds.

Unlike standard LLMs that generate probabilistic responses from indexed web content, ask-dig sources exclusively from the posts it collects and filters. Every insight is fully traceable to real social content, and every response includes source links so users can navigate directly from the answer to the original posts driving it.

The physical experience matters here. A user types a plain-language question into the interface. The system processes the query. Within minutes, results appear with clickable links back to source material. This is different from the black-box nature of most AI search tools where you get an answer but no way to verify where it came from.

Ofer Familier, CEO and Co-founder of dig, explained the core problem the platform addresses. Most AI platforms today produce answers that sound authoritative but lack grounding in real-world sentiment and behavior. Opinions are forming in conversations taking place through dynamic interactions across social, increasingly in videos. When that richness is flattened into clean text for analysis, critical context is lost.

The platform is built to bridge that gap. It delivers answers rooted in actual user-generated content so people can explore conclusions based on what others are genuinely saying and experiencing. This represents a fundamental shift from keyword-based monitoring to narrative intelligence.

ask-dig is part of dig's broader product suite, which includes an enterprise platform for always-on social intelligence. While ask-dig focuses on answering ad-hoc questions in real time, the enterprise platform handles continuous brand monitoring, narrative intelligence, deep social research, and consumer insights.

Legacy tools rely on keywords, mentions, and manual workflows. dig is designed for a video-first internet reality, enabling teams to track reputation, detect emerging narratives, and understand what is actually shaping perception across social at scale. The distinction matters as video becomes the primary vehicle for cultural discourse.

Use cases span multiple professional domains. An individual can search for a restaurant recommendation. A creator can ask for the latest trends. A journalist can map local reactions to a breaking story. A brand can get real-time feedback on a specific campaign. The platform is built for creators, journalists, consultants, marketing professionals, agencies, or anyone who needs fast, reliable, evidence-backed answers from social media.

Independent reporting from Martech Edge corroborates the core functionality and market positioning. The outlet notes that the platform's video-centric analysis layer 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. The rise of short-form video has fundamentally changed how narratives form and spread across platforms. 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.

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. This is an increasingly important requirement in the era of AI-generated summaries and synthetic content proliferation.

The platform attempts to correct 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 preserves 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. 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.

Pricing includes both a free tier and a paid entry-level tier at $100 per month. The official product page is available at ask.dig.ai. This pricing structure suggests the company is testing market fit while building toward enterprise adoption.

The official announcement from PRNewswire confirms the launch details and executive quotes. The document states that dig is the leader in video-first social intelligence, analyzing billions of posts across social platforms.

Whether this approach actually solves the verification problem remains to be seen. Users will need to test whether the source links genuinely lead to relevant content or if the filtering process introduces its own biases. The $100 monthly price point also raises questions about accessibility for individual creators versus enterprise teams.

Time will tell if ask-dig can deliver on its promise of evidence-backed answers. The technology exists, but adoption depends on whether users trust the filtering mechanisms and find the results more useful than existing alternatives. Whether users actually pay for it remains the real question.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn