How A Leading Retailer Uses Databrewery to Boost the Accuracy of its Natural Language Models

A global retail leader was looking to overhaul how it produced labeled data for its conversational AI and large language model applications. With chatbot interactions and a vast catalog spanning tens of millions of SKUs, the team needed a faster, smarter way to annotate chatbot conversations and product imagery without compromising on quality. By systematically labeling nuanced customer interactions, they aimed to strengthen their conversational AI models and create more human-like shopping experiences.

Previously, the company leaned on tech-enabled BPOs to handle data annotation. However, these setups quickly revealed limitations particularly around visibility and control. The labeling process operated like a black box, giving the in-house team little transparency into data quality or annotation workflows. Critical stakeholders ranging from ML engineers and data scientists to linguists and developers lacked access to collaborative tools for refining and iterating on training data. This hindered coordination, delayed feedback loops, and left subject-matter experts out of the equation.

Databrewery stepped in with a purpose-built platform that addressed these gaps head-on. With an intuitive, end-to-end workflow tailored for conversation-based AI, the team adopted Databrewery’s annotation suite for both text and image data. It offered robust tools to handle the complexities of NER tagging, entity linking, and dialogue labeling empowering data teams to label intricate conversations with precision. Whether it was free-form voice commands, short-form text, or hybrid GUI interactions, the platform handled diverse data formats seamlessly.

More importantly, Databrewery enabled a unified ecosystem where internal teams and external vendors could work collaboratively. The in-app review and quality assurance workflows allowed real-time feedback, while domain experts could actively participate in setting and maintaining labeling standards. This constant collaboration resulted in better training data, leading to higher-performing models.

Conversational AI in retail comes with layers of complexity. It's not enough for a system to recognize a request for “shampoo” ; it needs to understand whether the customer means anti-dandruff shampoo, sulfate-free, or a variant for dry hair. Each of these details requires precise labeling to ensure the AI correctly interprets the customer’s intent and surfaces the right product. This granular labeling capability proved critical to improving search accuracy and order fulfillment across voice-based and text-to-shop interfaces. With Databrewery, the company’s teams gained the ability to tag these nuanced distinctions within conversations, improving the system’s ability to fulfill requests accurately and naturally. 

The company also integrated Databrewery with its existing infrastructure particularly Google Big Query using the Python SDK to streamline large-scale data workflows. Manual orchestration gave way to automation, with labels being directly pushed and pulled from structured datasets. This tight integration simplified data movement and kept the labeling process aligned with real-time data pipelines.

The transition to Databrewery also brought visibility and operational efficiency that legacy systems couldn’t offer. Detailed project analytics allowed teams to track labeling performance, measure throughput, and detect issues early. With model-assisted labeling, the platform sped up annotation by letting the AI suggest labels, which human reviewers could fine-tune cutting down on manual effort without sacrificing accuracy.

Results were both tangible and measurable. Labeled data accuracy saw an estimated improvement of 25%, driven by Databrewery’s built-in QA tools, review loops, and collaborative workflows. When supported by Brewforce, the company’s preferred labeling service, this setup achieved 95% accuracy in labeled outputs while reducing turnaround time by nearly 25% compared to previous vendors. From powering the company’s flagship “Text to Shop” feature to enhancing customer service interactions and future LLM applications, Databrewery is now a foundational part of their AI data infrastructure. By enabling precise, scalable, and transparent labeling, it’s helping the enterprise build smarter, more responsive AI systems faster.