How are you currently handling AI tool complexity in your projects?

We built Chiranjeevi Tech because we kept seeing the same pattern — teams spending more time stitching together LangChain, vector databases, LLM APIs, and deployment pipelines than actually building their product. Curious to hear from others: how are you managing the AI tooling sprawl in your stack right now? Are you handling it in-house or outsourcing the integration layer?