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Migration
Chatbase gets a chatbot running in an afternoon. That speed comes with tradeoffs: a fixed retrieval strategy you can't tune, no visibility into what was retrieved for a given answer, per-message pricing that compounds at scale, and a hard limit on what the chatbot can do.
We migrate Chatbase chatbots to custom RAG systems, your chunking strategy, your retrieval logic, your model choice, and actions if you need them. You keep the knowledge base. You replace the platform that controls how it's used.
Tell us about your chatbot.
Four Chatbase constraints that a custom RAG resolves.
Chatbase uses a fixed retrieval strategy applied uniformly across your knowledge base. You can't tune chunk size, adjust overlap, change the embedding model, add reranking, or implement hybrid search. When the default retrieval strategy isn't finding the right context, there's nothing to adjust. The accuracy ceiling is built into the platform.
When Chatbase gives a wrong answer, you can see the output but not the retrieved context that produced it. There's no way to trace a bad answer back to a bad retrieval. Without retrieval visibility, you can't diagnose the problem. You can only observe the symptom.
Chatbase charges per message on higher tiers or caps messages on lower tiers. At production volume (thousands of queries per day) the per-message cost becomes a significant line item. A custom RAG deployment on your own infrastructure costs the LLM API calls directly, with no platform markup.
Chatbase is a question-answering interface. It can retrieve context and generate answers, but it can't take actions: look up a live record, create a ticket, update a database row, trigger a workflow. If your chatbot needs to do things, not just say things, Chatbase's architecture is a hard limit.
A custom RAG with your chunking strategy, retrieval logic, and model choice, plus actions if your use case needs them.
Chunk size, overlap, and segmentation logic tuned to your specific documents. Technical documentation, legal contracts, product manuals, and support articles each have different optimal chunking approaches. We test multiple strategies against your query set and use the one that performs best.
Dense retrieval, sparse retrieval, or hybrid, plus reranking if your query set benefits from it. Every retrieval decision is explicit in code, testable, and adjustable. When an answer is wrong, you can inspect exactly what was retrieved and why.
Use the model that makes sense for your use case and budget. Not locked to whatever Chatbase has chosen. If your queries are complex reasoning tasks, use a larger model. If they're mostly classification and extraction, a smaller model saves cost without quality loss.
Function calling and tool use integrated into the RAG pipeline. The chatbot can look up a live record before answering, create a support ticket, check inventory, or trigger any API you expose as a tool.
Custom RAG is an investment. It pays back when you're at the scale or accuracy requirements where Chatbase's constraints are real.
Chatbots under 500 queries per month with acceptable accuracy
If Chatbase is working well enough and your volume is low, the migration cost isn't justified. The accuracy and cost benefits of a custom RAG become significant at production volume. Come back when either the accuracy is genuinely limiting you or the volume justifies owning the infrastructure.
Teams who don't have a way to evaluate answer quality
A custom RAG is only better than Chatbase if you can measure the improvement. If you have no way to evaluate answer quality (no test set, no user feedback mechanism, no way to score outputs) the migration may produce a better system that you can't verify is better. We build evaluation into the migration, but you need some signal of what 'better' looks like.