Split the workflow into bounded steps
Separate document preparation, classification, extraction and final synthesis. Give each model step a clear input, expected output and failure path.
For example, classify a document before running a specialized extractor. Validate the extracted data before asking another model to produce a summary. This keeps one failed step from silently corrupting later output.
Match each step to a model
Use one approved model for simple classification and another for difficult analysis when the evaluation justifies it. Both requests can use the same TextCortex client.
Keep the mapping in configuration. Apply regional requirements to every step and avoid assuming that a replacement model inherits the previous model’s processing arrangement.
Validate outputs between calls
Use schemas and application rules to check model output before it becomes an input to another step. Route invalid output to a bounded retry or human review.
Record the document identifier, model and step outcome so you can diagnose failures without retaining unnecessary sensitive content in logs.
Connect the API to your orchestrator
Call the API from your own service, a queue worker or an automation tool such as n8n. Keep credentials in the execution environment and make external write actions idempotent.
Follow the n8n API guide for an HTTP example. The cost-per-task guide helps estimate a workflow with multiple model calls.