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Diagnostics and optimization of an n8n workflow for a medical chatbot that works with a vector knowledge base. The bot advises patients using up-to-date medical information.
A medical AI bot based on N8N advised patients by querying a vector knowledge base with up-to-date medical information. However, as the base and the number of requests grew, the scenarios began to work unstably, the response time increased, and errors appeared when processing requests.
We carried out a full diagnostics of the N8N workflow.
We identified bottlenecks in the chains of requests to the vector database, redundant calls, and suboptimal prompts. We rebuilt the scenario architecture, reduced the number of steps, optimized the knowledge base search logic, and set up caching of repeated queries. The result: the bot began responding faster and more accurately, and the workflow withstands a significantly higher load without failures.
Date
December 2025
Category
Process automationTechnologies
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