Hurix Digital Scales High-Accuracy Data Labeling for Conversational AI at Enterprise Level

For conversational AI systems, high-quality labeled data is the foundation of accuracy and reliability. Without precise intent tagging and consistent entity recognition, even the most advanced models can misinterpret user queries and provide a bad customer experience.

Our client, a fast-growing AI company creating virtual assistants for banking, healthcare, insurance, and retail, faced this challenge. They had to label over half a million utterances across domains in 6 weeks. Each dataset needed to be accurate, consistent, and audit-ready, a big task given the large and distributed team of annotators.

Previous annotation attempts had failed. Labels were inconsistent, intent categories were unclea,r and inter-annotator agreement was low, resulting in weaker model performance and missed deadlines.

Hurix Digital stepped in with a scalable annotation framework to solve these challenges. The results were:

• 500,000+ utterances labeled on time
• 98.7% inter-annotator agreement
• 23% model accuracy improvement
• 35% less rework, saving time and cost

Hurix Digital used its expertise and processes to help the client build smarter AI assistants and better customer experiences.

Download the full case study to see how structured data labeling can help your company’s AI!