Approach
"Working" and "production-grade" are different things.
Every system above shares the same underlying discipline — the difference between a workflow that impresses in a demo and one you can hand to a client's real traffic.
Input validation & sanitization
Bad, oversized, or malicious input is rejected before it reaches a database or an LLM call.
Structured, schema-validated AI output
The model can't return malformed data — every field is enforced against a schema, not hoped for.
Confidence-based routing
AI resolves what it's actually confident about, and hands off everything else with full context — not a binary bot-or-human split.
Global error handling & dead-letter logging
One error workflow attached across the system. Every failure is captured and audited, not silently dropped.
Retry logic on external calls
APIs time out. Every external call is built to handle that instead of failing the whole run.
Automated reporting, not manual dashboards
Leadership gets numbers in their inbox on a schedule — nobody has to remember to pull a report.
Reference deployment shape — the same skeleton underneath every build, adapted to the client's actual stack.