dig is expanding its video-first social intelligence platform by using Lovable to turn enterprise social data and recurring customer questions into custom applications tailored to specific workflows.

Turning Social Intelligence Into Custom Applications

The collaboration enables dig to build purpose-built intelligence applications in days rather than months. Instead of requiring enterprise teams to work through large volumes of dashboards, alerts, and raw social data, dig can package its intelligence into visual tools designed around specific business needs.

Its application suite already includes:

• Brand health monitoring

• Influencer discovery

• Crisis management

• Product benchmarking

• Narrative intelligence

• Campaign analysis

dig plans to expand the application framework to cover major enterprise use cases over the next year.

From Data and Alerts to Action

The applications are designed to make social intelligence directly actionable. One example involved a global luxury retail group that needed better prioritization and geographic context from its brand-monitoring data.

dig built a location-aware monitoring application that maps individual properties, identifies emerging issues, and connects those issues to the original social content. This allows teams to understand where a narrative is developing and potentially respond while an issue is still unfolding.

Lovable Speeds Up Application Development

Lovable provides the software creation layer that allows dig's teams to rapidly build the front-end applications on top of its existing intelligence infrastructure.

This means teams closest to customer problems can translate dig's data and AI capabilities into workflow-specific interfaces without lengthy technical implementation cycles.

Video-First Social Intelligence at the Core

The custom applications sit on top of dig's broader social intelligence platform, which continuously monitors brand reputation, competitive positioning, and emerging narratives across platforms including TikTok, Instagram, and YouTube.

Rather than relying primarily on keyword matching, dig says its platform analyzes tone, visual context, audience reaction, and cultural dynamics to interpret social content at scale.

The company reports 95% accuracy in tagging relevant posts and brand mentions and more than 90% coverage across the social landscape.

From Social Listening to Workflow-Specific Intelligence

The collaboration represents a shift from simply collecting social intelligence to embedding that intelligence directly into the workflows where enterprise teams make decisions.

In this model, the underlying social data and AI analysis remain centralized, while the applications built on top can be customized around individual customers' questions, processes, and operational requirements.