The Challenge
Engineering teams were spending too much time building internal infrastructure for genAI projects. Data was scattered across teams, leading to duplicated efforts, delays, and slow AI product rollouts.
From siloed systems to rapid AI deployment: how a leading enterprise scaled genAI faster
The Approach
They implemented Databrewery datas and Annotate to centralize training data, enable collaboration, and accelerate labeling with Databrewery Boost. This helped unify workflows across all genAI teams.
The Outcome
In just eight months, the team cut their labeling operations time in half. With cleaner data pipelines and faster collaboration, they accelerated deployment of generative AI features across products achieving a 5X increase in rollout speed. By centralizing their data tools, they turned a major bottleneck into a competitive advantage.
A global software leader in digital creativity and document solutions needed a way to unify its generative AI data strategy. For years, its products had quietly used AI under the hood, powering smart features that earned industry recognition. But as generative AI became a strategic priority, it was clear the current approach to data labeling, fragmented, manual, and inconsistent, couldn’t keep up with the pace of innovation.
One of the company’s most forward-looking R&D teams became an early adopter of Databrewery. Tasked with pushing boundaries in video understanding, document parsing, and multimodal learning, the team had previously built their own training data pipelines from scratch. This resulted in long delays and limited visibility across different groups working on similar problems. Databrewery gave them a single platform to collaborate across ML teams, internal reviewers, and external labeling partners, all while standardizing quality from day one.
That’s where Databrewery came in. Using its Annotate platform, the team integrated model-assisted labeling, dynamic annotation relationships, and a flexible image and text editor that allowed everyone engineers and historians alike to work in sync. With Databrewery, they sourced high-quality annotators who could keep up with both the scale and complexity of the work without requiring constant supervision.
“Being able to surface relevant data and label it in one place changed everything,” said Leo Martinez, Principal ML Architect. “It let us move from idea to prototype without the usual infrastructure headaches.”
Databrewery’s collaborative tools also helped bridge the gap between product and data science teams. Stakeholders could define their needs, and data teams could turn those into clear labeling instructions — adjusting ontologies as priorities shifted. With consistent QA processes and a tight feedback loop, teams stayed aligned throughout the project lifecycle.
Within just eight months, labeling times dropped by 50% and deployment speeds jumped 5X. New AI Assistant features built to understand PDF structure were rolled out in production, boosting product quality and reliability. And thanks to Databrewery Boost, the team now processes tens of thousands of PDF documents through a dynamic queueing system surfacing high-impact data faster and improving generative AI outputs with every iteration.