by Gediminas Sadaunykas
What if your phone’s personalization app came with its own smart sidekicks? In this friendly exploration, we’ll imagine a future Zedge powered by autonomous AI “agents” – little digital helpers that can act on our behalf – enhancing everything from how content is created, how the team receives insights from vast troves of data to how we plan product improvements.
Zedge is well known for helping people customize their phones with unique wallpapers and ringtones, and we embraced the AI era with our image generator, pAInt. Now, let’s get curious about how smarter AI agents could push us even further in three key areas: Content Creation, Data Insights and Content Curation. Grab a cup of coffee (or plug in your favorite new ringtone) and let’s dive in!
Creative Sidekicks
Creating content with AI often involves hidden levers and knobs –powerful parameters that shape the final result. These controls are intentionally kept out of users’ sight to simplify the experience while keeping the process stable and predictable. Now, imagine a personal creative assistant built right into Zedge – an AI agent that doesn't just understand how item generation works under the hood, but also understands you. Armed with a deep understanding of generative AI as well as insight into your aesthetic preferences, such an agent could potentially make the creation process smarter, faster, and more expressive – without adding significant complexity.
What could your creative AI assistant do? One of the defining properties of an agent is tool usage, with access to internet search being the most powerful.
Picture this: you ask for “a fun outdoor birthday party in Lithuania in August 2023, make it relevant to local events.” A standard AI generator would probably spit out a few happy faces, splashes of yellow-green-red, and some trees. But how could an agent enhance this? By scanning the web for local news, it would spot that Lithuania just won EuroBasket 2023 – so it adds basketballs bouncing around and fans waving scarves. Outdoors, you say? It checks the weather for August 2023 and finds those notorious summer showers, adds misty raindrops and dewy leaves, and voilà: a richly layered, hyper-personalized scene that feels rooted in your exact time and place.
And let’s not forget other media types. So maybe an agent could be a music maestro too – imagine typing, “upbeat electronic ringtone with a hint of retro game vibe” and the AI composes a catchy tune just for you. The agent might even learn your preferences over time (e.g. you love lo-fi wallpapers and acoustic guitar tones) and proactively suggest, “Hey, how about a custom chill wallpaper-ringtone combo for your Sunday mood?” The possibilities are endless and fun.
Rapid Insights for Our Developers
Every day, Zedge’s platform generates massive volumes of data – content uploads, downloads, search trends, revenue, and more. All that data presents a challenge: the volume, velocity, and variety make data quality monitoring a labor-intensive task. With scale comes complexity, and with complexity come such issues as consistency, duplication, and the lack of a single source of truth. This is where a data agent could step in – automatically surfacing anomalies, generating data quality reports, and providing quick, ad-hoc insights through familiar interfaces such as Slack.
Let’s say something goes awry – perhaps download counts plummet due to a server issue, or a particular wallpaper suddenly gets extremely popular out of nowhere – the AI can ping the team immediately: “Heads up: Today’s download rate is 30% lower than usual as of 10 AM, potential technical issue.” Or, “A new video wallpaper is experiencing a 10x spike in downloads – might be worth investigating or featuring.” This early warning system saves valuable time, as it’s far better to spot a sudden trend shift or a problem in source data in real time, rather than a week later in an official report.
Evaluating data quality often requires a wide range of tools to navigate complex, high-volume contexts. Data professionals rely on sharp machine learning models for anomaly detection and trend spotting, create extensive libraries of SQL queries, and develop expressive data visualization techniques. By isolating and integrating these different tools within a purpose-built agent, we can envision a future where daily, automated quality reports provide high-level summaries of newly ingested data, covering such metrics as completeness, consistency, and distribution drift. This would be incredibly valuable for both data engineers and scientists. With such oversight in place, it’s easy to image receiving a morning insights like “There’s a 5% uptick in ringtone downloads today, likely driven by a trending Bollywood track.”
Proactive Evolution - ‘The Enricher’
Digital content trends often have very short lifespans, shaped heavily by new developments on social media, in news, and entertainment. While platforms such as Zedge keep a finger on the cultural pulse, building more proactive connections to the outside world could provide a crucial competitive edge – helping us keep our content fresh and the user experience dynamic, fun, and adaptive.
Imagine an agent subscribing to design websites, art blogs, and pop culture news. This agent parses articles and posts about emerging graphic design styles, color palettes, or popular art motifs. For instance, if design blogs announce that “neon pastel gradients” are the upcoming design fad, the agent will catch it. Similarly, if a major movie or game release is generating buzz (leading to fan art and soundtrack excerpts trending), the agent could suggest related content themes such as wallpapers of those characters or ringtone snippets of the theme music. The content Zedge users see in the app would become ever more connected to what’s actually going on in the wider world.
One of the core objectives of this agent would be to enrich content metadata on Zedge. When it finds a trending keyword or theme, it can auto- tag any relevant Zedge content with that term, or create new tags/categories as needed. Good metadata is crucial for content discovery, as poor tagging can hide content from search. For example, if “#RetroWave” art is trending externally, the agent might suggest tagging applicable wallpapers on Zedge with the keywords “Retrowave, Synthwave,” ensuring users searching for those terms can discover all the items relevant to that trend easily.
By combining social sentiment analysis with competitor intelligence, the agent can proactively recommend UX and feature improvements. It might scan app store reviews, tweets, Reddit threads, or forum discussions about Zedge and competing apps to surface recurring pain points or user requests. For instance, if users on Reddit consistently ask for better search filters or more personalized feeds, the agent can highlight these needs as opportunities. Similarly, if a competitor rolls out a new UI feature – such as “swipe up for related content” – and it receives positive feedback, the agent can flag this as a potential enhancement worth considering or reprioritizing in the roadmap.
The Journey Ahead
Whether it’s co-designing stunning wallpapers, surfacing real-time trend whispers, or suggesting proactive behind-the-scenes improvements, these agents illustrate what’s possible when we embrace AI as a collaborator rather than just a tool. Of course, this vision isn’t without its challenges – ranging from the internal cost of running a creation agent, to the complexity of context management for a data agent, to the cultural and procedural shifts required to support an enrichment agent. Still, I believe the agentic revolution is just around the corner. Let’s keep asking, “What if?” – and together, we’ll keep turning those what-ifs into what’s next.