1. Understand Conversations While most NLU research focuses either on automated response to individual queries (e.g., intent systems, bots) or manipulating single-author texts (e.g., translation, summarization), AI Masteries is focused on identifying speaking behaviors and meanings that arise with multiple speakers.

2. Go Big with Small Data A common AI strategy is to perform mass data aggregation via a platform, then resell the learned domain model to platform users. AI Mastries is instead focused on generating customer-specific insight, and we measure ourselves by how quickly and easily we do so. We’ve designed our production and data pipelines to strictly segment by customer, supporting on-prem deployments if needed.

3. Leverage Human Supervision Customer-facing teams are subject-matter experts at their conversations, but are also operationally taxed teams that must prioritize conducting conversations over research. AI-Mastries job is to make the most of their expertise without disrupting existing processes or demanding too much time — at any point in our adoption process.