A Fortune 500 e-commerce company struggled with poor-quality training data that slowed its AI-driven personalization. Their previous provider leaned too much on automated labeling, consistently falling short of accuracy standards for production-ready models.
They switched to Databrewery’s curated pipeline, powered by Brewforce’s expert-led labeling. By blending automation with human-in-the-loop checks, they sped up annotation while maintaining precision. Model-assisted labeling accelerated the process, and structured review loops ensured consistent data quality.
With the new setup, the company saw a measurable lift in training data quality, enabling smoother AI development. Labeling velocity improved by nearly 50%, and AI initiatives that were previously on hold gained fresh momentum without sacrificing precision or reliability.
A prominent e-commerce company known for hosting online storefronts for handmade and vintage products set out to build several AI models to improve shopper engagement and personalization. These projects focused on image classification and object detection demanded accurate tagging for tens of thousands of product SKUs. Their initial partner, an AI-based labeling vendor, prioritized speed but consistently failed to meet the quality standards critical for production-ready models. Recognizing the need for tighter control and reliability, the company transitioned to Databrewery’s end-to-end data curation platform, working alongside Brewforce to access high-precision annotation services aligned with their model goals.
Given the scale and diversity of its AI initiatives, the company established an internal coordination hub within its data science team. This central point of contact was responsible for consolidating labeling requirements, approving data projects, and transferring them to the Brewforce operations team. With this system in place, different teams across the organization could now initiate and fulfill their data labeling needs in a structured, SLA-backed workflow. This not only introduced operational clarity but also fostered long-term alignment between internal stakeholders and external annotation experts enabling faster and more consistent execution.
Using Databrewery’s annotation tools, the company automated a significant part of their manual workflows through its Python SDK. Integration with their existing Google Cloud environment streamlined data imports, and seamless compatibility with BigQuery simplified the movement of structured data. The entire pipeline from label creation to export was now more agile and aligned with their broader MLOps framework. Even model training pipelines were integrated smoothly via Vertex AI, supporting continuous iteration.
One of the most impactful improvements came from intelligent automation. The team applied model-assisted labeling to handle object detection tasks more efficiently. They manually labeled an initial dataset, trained a baseline model, and used its predictions as pre-labels for future batches. This reduced the manual burden on human annotators while maintaining quality control. The result: a 50% increase in labeling speed with no compromise on accuracy. As the model's performance improved through ongoing training, the time and effort required to label new datasets decreased even further.
To maintain high standards, the data science team built in a strong feedback and quality review loop. Each batch of labeled data was reviewed for precision, and feedback was relayed quickly to drive iteration. Weekly sampling and QA checks became the norm during ongoing projects, ensuring that label accuracy stayed aligned with evolving model expectations. This tight feedback cycle helped annotators fine-tune their output continuously, improving project outcomes and speeding up deployment timelines.
With robust MLOps systems now in place and a reliable pipeline built with Databrewery and Brewforce, the company has significantly elevated its data quality. These upgrades have directly translated into faster AI development and more efficient operations. Their AI models trained on consistently curated, high-quality datasets now play a critical role in delivering personalized shopping experiences. Moving forward, the team is exploring ways to further optimize data visualization and integrate advanced training workflows using Databrewery’s Catalog and Model modules.