To summarize moderation content in a Node.js app, use semantic embeddings to select evidence and GPT to produce a schema-constrained summary, then decide which text may cross which processor boundary, for how long, and in what form the human reviewer can trust. The useful design is an embedding-and-retrieval step before the GPT prompt, with the original report and its deletion policy under your control.

That constraint changes the vendor decision. Use a direct model provider when its contractual and regional controls match the report's requirements. Use a platform such as Infrai when a single consistent API and self-describing capability surface reduce integration work across a broader backend. Keep the data boundary explicit either way; an embedding endpoint does not magically provide residency or deletion guarantees.

What does the embedding actually protect?

An embedding is a representation for semantic comparison, not a redaction layer.…