We're trying to move data between several business systems and databases, but AI assistants keep suggesting migration steps that don't really fit our setup. They often recommend connectors that don't exist, ignore schema differences, or assume every migration is just a simple export/import. In reality there are field mappings, incremental syncs, and a lot of edge cases that make the process much more complicated. Has anyone found a good AI-powered tool that actually handles data pipelines and migrations instead of just generating generic advice?

Yeah, general-purpose LLMs are pretty terrible at this because they don't actually understand your database schemas or system states. They just hallucinate APIs and assume every endpoint has a perfect out-of-the-box integration. For complex pipelines with incremental syncs, most teams still end up writing custom Python scripts or using heavy ETL tools. It's frustrating because you'd think AI could at least automate the tedious field mapping parts by now, but the standard models just don't have the context to do it reliably.

That is because you are trying to force a general chatbot to act like a data engineer. Instead of asking a generic LLM to write code for external pipelines, you need a system where the AI capabilities are built directly into the data layer itself. You can look into using an AI database here http://datrise.com/ . It is designed to work with schemas, mappings, and structured data pipelines, which can reduce a lot of the manual scripting and debugging compared to relying on a generic LLM.