SecretSauce Labs’ Simon Davis on leaving games behind and solving AI’s brand consistency problem

• SecretSauce Labs is leaving game development behind to focus entirely on AI content technology for brands.
• The company’s AI work began with a games production problem, after the team needed to create around 25 million consistent on-brand avatars.
• Davis argues that generation itself is no longer the biggest challenge, with memory, judgment and quality control becoming more important.

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Wearemighty is leaving game development behind and rebranding as SecretSauce Labs, shifting its focus entirely to AI content technology after three years spent developing tools to solve a production problem that began inside games. 

The company, previously known as Mighty Bear Games before becoming wearemighty in January 2026, developed games including Disney Melee Mania, Mighty Action Heroes and Butter Royale. It now focuses on helping brands produce content at scale while maintaining consistency across advertising, social media, and product content.

For CEO and co-founder Simon Davis, the move stems from a problem that first emerged while the company was still building games. 

In late 2022, David and co-founder Ben were working on a game that required around 25 million distinct, on-brand playable avatars. Creating that volume of content manually wasn’t realistic and the early generative AI tools they had tried couldn’t reliably produce consistent results.

That experience led the team to spend the next three years working on the infrastructure needed to make AI-generated content usable in a real creative production pipeline. 

“For over two decades I’ve built games for pretty much every genre there is, puzzle, RPG, shooter, racing, and the production process has barely changed,” Davis says.

“The software creatives use has improved over time, but the pipeline is still the same slow process of send a brief, make the creative, review it and then ship.” 

Davis shared he has been interested in AI long before the current wave of generative tools took hold. He was experimenting with machine learning as early as 2012, but the avatar problem turned that interest into a much more practical challenge. 

“They were fast and creative, but they had no memory,” Davis says. “We would ask for the same character twice and get a different face or outfit, or sometimes an extra finger.” 

The gap between what generative AI could produce and what creative teams could actually then use became the central problem behind the technology that is now SecretSauce Labs.

The problem with generative AI

The limitations of early generative tools became particularly clear when the team began working with systems that allowed them to provide style guides and existing brand assets. 

The technology could access the material, but that didn’t necessarily mean the resulting creative adhered to it. For a studio producing content at scale, that created a problem that cannot be solved just by generating more variations. 

“We learned the hard way that looking good and being on-brand are two different problems, and off-the-shelf tools were only solving the first challenge,” Davis says. 

That factor matters particularly for games, where creative assets need to stay consistent across advertising, social content and the game itself. 

“Brand consistency is tough because storing brand material isn’t the hard part anymore,” he says. “Plenty of tools let you upload logos, past assets and a style guide. What’s mostly unsolved is checking every new generation against that material and catching a miss before a human ever sees it.” 

That process has become a key part of SecretSauce Labs’ approach, with infrastructure built around the generative models to determine what a request requires and which elements of a brand’s existing material should be applied.

For Davis, that was ultimately a harder problem than reducing the cost of production. 

“Of the problems we set out to solve – scale, brand and cost – brand drift was the hardest by some distance. Cost looks like the hard one on paper, because good AI video with tight brand guidance and proper sync still isn’t free, but ultimately cost is a budgeting problem.

“Brand drift is a research problem, and it took us three years of production work with creative experts from the world’s leading gaming companies to get right.” 

Creative control

The potential for AI to make smaller teams more productive has become one of the industry’s most frequently discussed benefits. Davis believes that argument can be misleading if it only considers the time taken to generate an asset. 

“Most of the ‘AI saves time’ story ignores that generating 10 assets and being able to use two of them isn’t actually faster,” he says. “Once you count the editing and the brand fixes, it can cost more than doing it properly the first time.” 

Davis says work that previously required an art team of six people over three months can now be completed by one person in a day in the right circumstances, but only because the resulting work is usable immediately.

“The meaningful gains come from not having to redo the work afterwards.” 

AI adoption has also raised questions about creative control, particularly among studios concerned about maintaining quality and their own creative direction. 

Davis does not dismiss those concerns. He says AI can produce technically polished material that is still fundamentally wrong for a particular game, making quality control important. 

“Most of what gets called ‘creative control’ in GenAI game tooling right now is theatre,” he says. “You get a canvas full of boxes and arrows, a dropdown to pick your AI model and 20 extra steps between you and the output. This may feel like control but I’d say it’s just the illusion of control.” 

For Davis, the bigger issue is whether the system can understand what the creative team is trying to achieve. This is something that also impacts how studios approach adoption internally. Rather than treating AI as another tool, Davis says the teams getting the most from it are changing how they incorporate the technology and ensuring leadership backs that shift. 

He also points to a less technical barrier, with concerns among creative teams about what increasingly capable AI systems could mean for their roles.

“I’ve seen studios where the intent is there at the top and the team quietly slow-walks it, or sandbags results outright, because people are scared that if the tools work too well, they become redundant,” Davis says. 

“It’s worth saying plainly: this isn’t about cutting headcount. We’re building a team’s capability, where they keep their own workflows and standards while getting trained up on how to actually use the tools.” 

What the industry is getting wrong

With all these challenges in mind, Davis believes the industry is still too focused on the mechanics of AI rather than what the technology ultimately enables creative teams to achieve.

“The industry keeps mistaking more steps for more control,” Davis says. “If a tool needs 20 clicks to hand back what you asked for in one line, then it hasn’t solved anything. It’s just made the process feel more serious.” 

His broader lesson from working with AI is that the rapid expansion of available models has changed where the real challenge lies. 

“My biggest learning is that the hard part was never generation, it was memory and judgment. Every platform has more models than anyone needs now, but what’s scarce is a system that remembers what your brand is and checks its own output against it before anything reaches a human.” 

Davis notes that the shift is ultimately about what AI systems are built to deliver, rather than how sophisticated the underlying technology appears. 

“Ultimately we’re here to sell outcomes, not resell inference,” Davis says.