See how ASAPP speech scientists recently developed the most accurate ASR model. Read more

While designing AutoCompose, the ASAPP design team turned down the noise in the agents’ workflow to give them a UI that signals the right suggestions at the right time. Read more

Where is automation the most impactful? Our AI models specialize in automating even the most difficult parts of customer interactions. Read more

Empower your leaders to make data-driven, impactful improvements in the call center. JourneyInsight helps you understand customer problems and which tools agents use. Read more

For conversational AI to advance, the broader scientific community must be able to work together and explore with easily accessible state-of-the-art baselines for fair performance comparisons. Read more

Is your call summarization strategy failing you? Learn from a large enterprise contact center’s mistakes. Read more

Understanding the level of an AI system can help predict how the system will change over time – whether it will continuously improve, remain the same, or even degrade. Read more

End-to-end architecture improves the scalability and performance of machine learning models for speech transcription to serve enterprise contact center needs better. Read more

Wav2vec 2.0 is arguably the most popular approach for using self-supervised training in speech, but it could be more efficient. We introduce SEW for better efficiency and performance. Read more

By combining an intuitive user experience with AI-driven recommendations, you can radically improve the onboarding process. Here’s our approach to making agents successful, faster. Read more