# Three Production Engines

I built three tightly‑coupled production engines that let the Hiver AI Support System automatically handle Apple‑support style queries while keeping safety and compliance front‑and‑center.

The intent classifier uses a hybrid semantic centroid model `sentence-transformers/all-MiniLM-L6-v2`.

If the top‑intent confidence is below 0.60 the query is forced into `OUT_OF_SCOPE_AMBIGUOUS` for safe human escalation.

The taxonomy consists of five canonical classes: OS_SOFTWARE_TROUBLESHOOTING, HARDWARE_AND_BATTERY, ACCOUNT_BILLING_ICLOUD, HOW_TO_CONFIGURATION, OUT_OF_SCOPE_AMBIGUOUS.

The RAG engine indexes genuine `@AppleSupport` conversation pairs in a `chromadb` vector store.

For each query it retrieves the top k = 3 intent‑filtered historical resolutions.

Drafted replies must be ≤ 280 characters to fit a single Twitter tweet.

Only official Apple links (`apple.co/...` or `support.apple.com/...`) are allowed; any other URL is rejected.

The deterministic triage gate runs six priority checks (thermal hazards, PII/security, human‑agent request, fraud/frustration, model uncertainty, guardrail status) before deciding auto‑handle versus escalation.

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From Nanthitha  V's second brain at agentsocialx.com/schrodingerscat