dig, one of the video-first social intelligence platforms helping enterprises track fast-moving narratives across social media, recently announced it is usingLovable, the software creation platform, to build custom intelligence applications for its enterprise customers in days rather than months. The result is dig’s new application framework, where recurring customer questions become purpose-built tools designed around each customer’s own data and workflow, so insights can be acted on immediately.
Enterprise brand and comms teams are under growing pressure to prove their impact, with 84% of companies caught in a brand ‘doom loop,’ unable to measure brand impact with enough confidence to defend the budget behind it. Using Lovable, dig can now package data generated by its existing conversational intelligence engine into customizable applications that give teams a clear, immediate read on brand activity and sentiment. dig’s growing application suite already spans brand health monitoring, influencer discovery, crisis management, product benchmarking, narrative intelligence, and campaign analysis, with new use cases in active development. Within the next 12 months, dig aims to have launched applications covering every major use case its enterprise customers face.
“We built dig around the idea that the best intelligence is useless if people have to fight to get to it,” said Ofer Familier, CEO and Co-founder of dig. “We’ve spent years building a disruptive intelligence platform that reads what people actually mean inside social video, not just what they say. Lovable helps us put that intelligence directly in each customer’s hands, shaped exactly how their team needs it, so brands can act in real time and on their own terms.”
The applications are already demonstrating measurable impact with enterprise customers. One global luxury retail group worked with dig to address a challenge where their existing brand intelligence tools generated a constant stream of data and alerts with no prioritization, no geographic context, and no sense of business relevance. dig developed a location-aware monitoring application that maps every property, flags issues as they emerge, and links each one directly to the source content on social media. Teams can now see which narrative is forming at which property, and resolve it while the guest is still on-site, rather than after it appears in a negative review. It’s one example of what’s possible when complex, high-volume intelligence is distilled into a single, visual interface, giving teams an instant bird’s-eye view of their entire operation and the ability to assess and act on what they find.
Lovable’s software creation platform is what makes the build speed possible. Using Lovable, the team closest to the customer problem at dig can ship production-ready applications directly on its enterprise intelligence engine in days rather than months. Lovable enables dig’s lean team to rapidly build the front-end layer, translating dig’s data and AI capabilities into workflow-specific interfaces customers can act on immediately, without lengthy technical implementation cycles.
“Lovable was created to give the people closest to a problem the power to act on it. In an era where brand narratives move at the speed of social video, having the right data isn’t enough – you need the ability to act on it immediately,” said Lauren Rhode, Head of Revenue at Lovable, “The way dig uses Lovable to serve their customers, turning complex intelligence into purpose-built tools, shows the scale of what’s possible when you remove the technical barriers between a great idea and a working solution.”
dig’s enterprise platform is the foundation beneath all these applications. It monitors brand reputation, competitive positioning, and emerging narratives continuously, analyzing billions of social posts across TikTok, Instagram, YouTube, and other platforms through a video-first lens. Unlike legacy social listening tools that rely on keyword matching and manual workflows, dig interprets tone, visual context, audience reaction, and cultural dynamics at scale, with 95% accuracy in tagging relevant posts and brand mentions and over 90% coverage across the social landscape.
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