Customer feedback can arrive in different languages and still need to reach the right operational team. A useful automation does more than assign a sentiment label: it captures the message, interprets it within a defined scope, creates a trackable case, and makes ownership clear.
My portfolio documents a BMRC Hospital workflow connecting multilingual feedback processing with ticket routing and Gmail notifications using n8n, OpenAI, Supabase, and Gmail API. The published account describes operational feedback routing. It does not claim clinical decision-making, a language-by-language accuracy score, or a measured reduction in response time.
Design around the team's next action
Define the destination categories, the person or queue responsible for each one, and the information that must accompany a routed item. Keep categories actionable: a label that does not change who responds or what happens next may add complexity without helping the team.
A typical flow receives a message, attaches a stable identifier, classifies its topic, applies routing rules, stores the result, and notifies an owner. The exact steps depend on the systems and process already in use.
Separate interpretation from routing rules
Language models can help interpret free text, but important workflow rules should remain explicit. Required fields, permitted destinations, duplicate handling, and escalation conditions are easier to inspect as deterministic checks.
An uncertain classification can go to a review queue rather than being routed as if it were certain. Preserve the original message or a protected reference so a reviewer can check the context.
Protect information and evaluate the whole route
Feedback may contain personal or sensitive details. Decide what each system needs, apply access controls, retain only necessary data, and avoid exposing message contents or credentials in broadly available logs. Review privacy, retention, and vendor requirements before deployment.
Assess routing quality separately from model output. Check whether messages reach appropriate teams, how often a case needs reassignment, and what happens when a service is unavailable. Report results by language only when reviewed data supports that breakdown.
Multilingual feedback automation is an operations workflow, not just a sentiment prompt. The [BMRC Hospital case study](/case-studies/bmrc-hospital-ai-feedback-agent/) describes the portfolio implementation and its evidence limits.