A support assistant that validates every inbound message, identifies the customer, retrieves grounded answers from a vector knowledge base, and routes on confidence — auto-resolving what it's sure of, and escalating everything else with full context.

Support teams answer the same questions on repeat while complex cases sit in a queue. Leadership has no daily visibility into volume, resolution rate, or sentiment without someone manually pulling numbers. And a single unhandled error — a bad payload, an API timeout — can silently drop a customer conversation with no trace.

Three connected pieces. First, the assistant itself: validate input → identify or create the customer record → classify intent, urgency, sentiment, and confidence → retrieve relevant knowledge base content via vector similarity → generate a grounded response → route on a confidence threshold. Second, the escalation path: below that threshold, the system creates a support ticket, notifies the team in Slack and email, and hands the human agent an AI-written summary instead of a blank conversation. Third, observability: a global error handler attached to every workflow writes a dead-letter audit record and alerts support ops on any failure, and a 7am scheduled workflow computes yesterday's resolution rate, escalation rate, sentiment, and response time — then emails leadership a written summary automatically.

Input validation and sanitization — rejects spam and oversized input, strips control characters, before anything touches the database

Parameterized SQL throughout — no string-concatenated queries

Structured, schema-validated AI output — the model can't return malformed data

Confidence-based routing instead of a binary chatbot-or-human split

Retry logic with backoff on every external call

alwaysOutputData on the analytics query, so the daily report runs — with honest zeros — on a quiet day instead of crashing

Global error handler with dead-letter logging attached to the whole system

Automated executive reporting — no one has to ask for the numbers

Figures are evidence-based expectations for this architecture — happy to run this against your real ticket volume and report live numbers.