I am setting up a series of n8n workflows to automate natural language processing across my product. Text arrives from two main sources—live APIs and our internal databases—and I need a streamlined flow that ingests this data, runs it through an NLP model, then stores or forwards the results for downstream use. The core of the job is to design, build, and document these workflows inside n8n. That includes choosing the most appropriate NLP node or external micro-service, handling authentication for the APIs and database connectors, mapping payloads in and out of the model, and making sure everything is resilient and easy to maintain. Deliverables • Fully functional n8n workflow(s) that pull text from the specified APIs and databases, run the chosen NLP task, and output structured results. • Clear, step-by-step documentation (Markdown or inline) covering node configuration, environment variables, and how to adapt the flow to new endpoints. • A short hand-off session (recording or live) walking through the setup so future changes can be made without guesswork. The implementation is accepted when the workflow runs end-to-end on my n8n instance, processes a sample dataset from both sources, and returns the correct NLP output without manual intervention.