The Facebook insider building content moderation for the AI era

The Facebook insider building content moderation for the AI era

When Brett Levenson left Apple in 2019 to steer enterprise integrity at Facebook, the social media big was in the thick of the Cambridge Analytica fallout. At the time, he thought he might merely fix Facebook’s content moderation downside with better technology. 

The downside, he rapidly realized, ran deeper than technology. Human reviewers had been anticipated to memorize a 40-page coverage doc that had been machine-translated into their language, he said. Then they’d about 30 seconds per piece of flagged content to resolve not just whether or not that  content violated the guidelines, however what to do about it: block it, ban the consumer, restrict the unfold. Those fast calls had been only “slightly better than 50% accurate,” according to Levenson.

“It was kind of like flipping a coin, whether the human reviewers could actually address policies correctly, and this was many days after the harm had already occurred anyway,” Levenson told TechCrunch.

That form of delayed, reactive strategy just isn’t sustainable in a world of nimble and well-funded adversarial actors. The rise of AI chatbots has only compounded the downside, as content moderation failures have resulted in a string of high-profile incidents, like chatbots offering teenagers with self-harm steerage or AI-generated imagery evading security filters.

Levenson’s frustration led to the concept of “policy as code” — a method to flip static coverage paperwork into executable, updatable logic tightly coupled to enforcement. That perception led to the founding of Moonbounce, which announced on Friday it has raised $12 million in funding, TechCrunch has solely realized. The round was co-led by Amplify Partners and StepStone Group.

Moonbounce works with corporations to supply an extra security layer wherever content is generated, whether or not by a consumer or by AI. The company has skilled its personal large language mannequin to have a look at a buyer’s coverage paperwork, consider content at runtime, present a response in 300 milliseconds or less, and take action. Depending on buyer desire, that action might appear like Moonbounce’s system slowing down distribution while the content awaits a human review later, or it would block high-risk content in the second. 

Today, Moonbounce serves three main verticals: Platforms coping with user-generated content like relationship apps; AI corporations building characters or companions; and AI picture turbines. 

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Moonbounce is supporting more than 40 million daily reviews and serving over 100 million daily lively customers on the platform, Levenson said. Customers embrace AI companion startup Channel AI, picture and video technology company Civitai, and character roleplay platforms Dippy AI and Moescape. 

“Safety can actually be a product benefit,” Levenson told TechCrunch. “It just never has been because it’s always a thing that happens later, not a thing you can actually build into your product. And we see our customers are finding really interesting and innovative ways to use our technology to make safety a differentiator, and part of their product story.”

Tinder’s head of belief and security lately explained how the relationship platform makes use of these kinds of LLM-powered providers to achieve a 10x enchancment in accuracy of detections.

“Content moderation has always been a problem that plagued large online platforms, but now with LLMs at the heart of every application, this challenge is even more daunting,” Lenny Pruss, basic companion at Amplify Partners, said in an announcement. “We invested in Moonbounce because we envision a world where objective, real-time guardrails become the enabling backbone of every AI-mediated application.”

AI corporations are dealing with mounting authorized and reputational stress after chatbots have been accused of pushing youngsters and weak customers towards suicide and picture turbines like xAI’s Grok have been used to create nonconsensual nude imagery. Clearly, security guardrails internally are failing, and it’s changing into a legal responsibility query. Levenson said AI corporations are more and more wanting outdoors their very own partitions for assist beefing out security infrastructure. 

“We’re a third party sitting between the user and the chatbot, so our system isn’t inundated with context the way the chat itself is,” Levenson said. “The chatbot itself has to remember, potentially, tens of thousands of tokens that have come before…We’re solely worried about enforcing rules at runtime.”

Levenson runs the 12-person company together with his former Apple colleague Ash Bhardwaj, who beforehand constructed large-scale cloud and AI infrastructure across the iPhone-maker’s core choices. Their next focus is a functionality called “iterative steering,” developed in response to instances like the 2024 suicide of a 14-year-old Florida boy who grew to become obsessive about a Character AI chatbot. Rather than a blunt refusal when dangerous subjects come up, the system would intercept the dialog and redirect it, modifying prompts in real time to push the chatbot towards a more actively supportive response.

“We hope to be able to add to our actions toolkit the ability to steer the chatbot in a better direction to, essentially, take the user’s prompt and modify it to force the chatbot to be not just an empathetic listener, but a helpful listener in those situations,” Levenson said. 

When requested whether or not his exit strategy concerned an acquisition by a company like Meta, bringing his work on content moderation full circle, Levenson said he acknowledges how effectively Moonbounce would match into his outdated employer’s stack, in addition to his personal fiduciary duties as a CEO. 

“My investors would kill me for saying this, but I would hate to see someone buy us and then restrict the technology,” he said. “Like, ‘Okay, this is ours now, and nobody else can benefit from it.’”

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