In my experience building intelligence in psychology at Headspace (and really any consumer vertical), it comes down to three pillars when serving users and getting the ai product right. First is the interface… how users engage with the system across text, voice (@Vapi_AI is a great configurable platform here), multimodal, or interactive surfaces. The right medium depends entirely on the vertical and how people naturally express themselves. Second is the intelligence… planning, memory, domain expertise, agent orchestration, evals, and proactivity that guide someone through a continuous journey. My tool of choice here is langgraph from @langchain . It’s just a great state space machine and very pythonic. But for eval and safety, I feel one has to do their own work to build stuff for their specialized domain. Third is runtime and platform … low latency, scalable inference, reliability, and self-serve tooling( again depending on the use case - we often bias for realtime. This often includes a mix and match of training and inference environments to get the right performance footprint. For inference I am really like @SimplismartHQ combined with any training platform- honestly I don’t really mind sagemaker from @awscloud here but it can be a bit expensive. If it is, I bias towards using @runpod .