From Stock Library to AI Content Infrastructure
When Robin Ayoub first read that Magnific processes 175 million images and videos every single month, he stopped cold. That number does not just represent scale. It represents 175 million moments where a brand is trying to say something specific to a specific person in a specific language somewhere in the world. That tension between volume and meaning is exactly what drew him to invite Jose Florido, Chief Growth Officer at Magnific, formerly known as the FreePik Company, onto episode 247 of the Localization Fireside Chat. Jose joined the conversation from Beijing at two in the morning, which tells you something about how seriously the company is pursuing its APAC expansion strategy. The rebrand to Magnific is recent and deliberate. It signals a company that has moved well past being a stock image library and is now positioning itself as a full AI content platform covering image generation, video creation, and audio production including multilingual voiceover. Jose traces the strategic turning point back to when OpenAI released DALL-E 2. Before that moment, generative image models felt like novelty demos. After it, the Magnific team understood that the category they had built over fifteen years was being fundamentally disrupted, and they chose to lead the disruption rather than resist it.
What Controllable AI Actually Means in Practice
The phrase controllable AI carries a lot of weight in this conversation, and Jose is careful to unpack it honestly. The underlying models are inherently non-deterministic. Any experienced user of Gemini or DALL-E knows the frustration Robin describes of spending hours crafting a perfect prompt only to get wildly inconsistent results on the final step. Magnific’s answer is not to promise the model will behave. Instead, they operate at the application layer, giving users tools to constrain and guide the generation rather than hoping the model self-corrects. This means building libraries of reusable brand components, characters, backgrounds, color palettes, and approved prompt structures that get applied consistently across every generation. Their enterprise tier extends this further with collaborative workflows, approval and review flows built directly into the platform, and legal indemnity inherited from the company’s stock photography heritage. If a generated asset turns out to infringe on someone else’s intellectual property and the user never intended that infringement, Magnific will provide legal protection. That is a meaningful commitment for marketing teams inside large brands who are already cautious about AI-generated content. Jose is also candid that for genuinely nuanced localization work, like adapting a campaign for Saudi Arabia or Japan, human review remains essential. The model gets you ninety percent of the way there. The last ten percent still requires someone with real market knowledge.
The Harder Sell and the Bigger Opportunity
Jose identifies brands as the most difficult segment to convert, and his reasoning is worth sitting with. Video production companies and agencies see the ROI of AI almost immediately because they are close to the production itself. Brand marketing teams have historically outsourced production to agencies, so adopting Magnific requires them to take on a new mindset, new workflows, and sometimes new internal hires. This is precisely why Magnific built out a professional services team as one of the three pillars Jose oversees alongside enterprise sales and growth marketing. That team of AI-specialized creatives helps brands transition from managing agency budgets to actually directing AI-assisted production themselves. The commercial argument Jose keeps returning to is not about cutting costs. It is about expanding creative ambition. A brand that previously had the budget for a single thirty-second green screen ad can now produce longer format episodic content, multiple cultural variations of the same campaign through Magnific’s Spaces workflow tool, and localized voiceovers across dozens of languages. The constraint was never creativity. It was production capacity. AI removes that ceiling, but only if the people using the tools understand they still own every decision and every output that goes out the door.
This is a conversation worth experiencing in full. If you want to see Robin navigate the Magnific interface live during the recording, including the moment he logs in with a Google account and starts exploring image generation for his own YouTube thumbnails, Watch on YouTube. If you prefer to take the discussion with you on your commute or your next walk, Listen on Simplecast and catch every honest exchange between two people meeting for the first time and finding they have a lot to say to each other.
Robin Ayoub 
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