We do! We have combined experience as PMs, ml engs, and data scientists across many verticals. We also have experience helping PMs and AI eng teams build agents across over 100 customers from our first product.

You're totally right, the analytics annotation primitives we detect (intents, corrections, resolutions) are the cornerstone to all the other analysis in our platform. It's critical that we get those right or all the data and insights in the world are useless.

LLMs are a core part of that detection, but we also do things like hierarchical classification, (https://voker.ai/blog/hierarchical-text-classification-with-...) and will eventually add in other ML methods where applicable. On top of our automated detections, we're building ways for the annotations to improve and adapt to your specific agent product, your data, and your feedback on our annotations.

Our SDK is architected to eventually accept any type of event you want to send as additional information like add to carts, or other conversion metrics that are valuable for analysis on agent performance.

You're definitely right, we don't expect a PM to instrument this all themselves - similar to web analytics or product analytics tools, the engineering team instruments and maintains the integration, and then our app makes the insights and data accessible to not just the engineer but the whole product team.