Ahead of a major AI product launch, the team needed a fast, scalable solution for content moderation, specifically to tag safe vs. unsafe content. They wanted to avoid being locked into a single labeling vendor due to switching delays and vendor risk. The chosen platform also had to meet strict security and compliance requirements.
They selected Databrewery for its ability to support multiple labeling vendors in one workflow. This flexibility reduced risk and ensured they could adapt quickly if needed. The platform also met all enterprise-grade security and compliance standards required for deployment.
In just three months, the team labeled hundreds of thousands of assets for their content moderation pipeline. Databrewery helped them hit launch timelines, meet compliance needs, and maintain full control over vendor flexibility.
A leading AGI research lab was running dozens of experimental projects, each requiring massive volumes of labeled data. Their internal labeling tool had become too resource-intensive to manage, and as they moved closer to launching a large-scale AI product, they needed a faster and more flexible solution to review AI-generated content especially for content moderation.
Avoiding vendor lock-in was a core priority. Relying on one provider in a high-stakes environment meant increased risk, higher costs, and less flexibility to scale. To maintain research speed and resilience, the team chose Databrewery, which enabled them to contract, test, and manage multiple labeling vendors in parallel. Within weeks, they were coordinating over ten active vendors minimizing operational risk at a critical point in product development.
Security was another non-negotiable. With sensitive research and model outputs on the line, the platform had to meet strict compliance requirements. Databrewery provided enterprise-grade infrastructure that passed their rigorous vetting process and supported secure, reliable workflows across their ML programs.
Setup was fast and required minimal engineering support. Compared to prior tools, which delayed project launches, Databrewery let them configure image and text labeling projects in minutes. With support for webhooks and file attachments, the team easily delivered context to labelers and adjusted task guidelines on the fly. As project needs evolved, so did their workflows without starting from scratch.
Over just three months, the team reviewed and generated hundreds of thousands of annotations for their content moderation use case, flagging safe vs. unsafe content at scale. They integrated real-time feedback loops to update models and automatically flag violent, lewd, or toxic images. This accelerated detection and removal processes ensure a safer AI product.
The result was a highly successful launch of their generative AI application, earning strong user adoption and industry-wide attention. With Databrewery, the team was able to move fast, stay secure, and scale responsibly all without compromising research momentum.