The company’s previous data labeling provider couldn’t meet the quality standards needed for their NLP and LLM projects. Despite extra oversight from their data science team, low-quality annotations slowed AI development and drained internal resources.
They switched to Databrewery, using its platform and Boost services to streamline labeling workflows and improve collaboration with annotators. With Brewforce, they gained access to skilled labelers who consistently delivered accurate data without constant hand-holding.
Within a year, labeling quality improved by 20%, and costs dropped by 41%. Their AI team now spends less time on data prep and more time building and accelerating progress across their most critical AI initiatives.
The company develops AI-powered solutions that enhance business communications including features like transcription, summarization, and sentiment analysis, all driven by sophisticated NLP and LLM models. To build and maintain these systems, the team relies heavily on high-quality training data, subject matter expertise, and precise model fine-tuning. In the past, they worked with a legacy labeling provider that used a crowdsourced model to generate large volumes of data quickly. But as their AI efforts grew in scale and complexity, the limitations became clear: the data lacked the accuracy needed to support long-term success. Labels were often missing or incorrect, and as a result, model performance suffered.
Initially, the team tried to fix the issue by investing more of their own time redesigning labeling workflows, assigning specialized internal resources, and working more closely with the provider. But even with these extra efforts, the output still didn’t meet their standards. Over time, the process became so taxing that data scientists began hesitating to even request new datasets, knowing the effort it would take to get usable results. “It got to a point where our team would delay or avoid asking for labeled data altogether. It just took too much time and too many back-and-forths to get something usable,” said Miguel Tran, Senior ML Engineer at the company. After several years of declining data quality and growing internal strain, the team started looking for a better solution. Their two main priorities were clear: they needed reliably high-quality training data and a process that required far less time and effort from their internal teams.
They turned to Databrewery, a software-first solution designed for efficient, accurate labeling at scale. With Databrewery, the team gained transparency into their projects, more control over quality, and a faster path from project kickoff to results. They also leveraged Databrewery Boost to source expert annotation teams through Brewforce, eliminating the need for constant internal supervision. One year into the switch, the improvements were substantial. Data accuracy went up, turnaround time went down, and the entire team became more productive and motivated. Data scientists were once again requesting training data confidently, knowing it wouldn’t create a bottleneck. What surprised them most? The cost savings.
“We expected to pay more for better quality, but the opposite happened. We’re spending less than before and getting cleaner, more reliable data,” said Tran.
By moving away from crowdsourced labeling and adopting a more structured, quality-focused approach with Databrewery and Brewforce, the company unlocked faster AI development while protecting their data, reducing internal workload, and cutting costs.