For business
What it is
Quality control at the door: duplicates are dropped, broken or incomplete inputs are rejected with a reason.
Why it matters
Rejecting a bad input in milliseconds is far cheaper than explaining a wrong report a week later.
Risk without it
Duplicate meetings produce duplicate reports; empty transcripts produce confident nonsense.
For engineers
How it works
Schema validation, content checks (minimum length, detected language, audio duration against transcript length) and idempotency: a content hash or source id makes reprocessing safe. Rejections go to a review queue with the reason attached.
Typical stack
- Zod / JSON Schema
- SHA-256 content hashes
- DynamoDB conditional writes
Pitfalls & trade-offs
- Fail loudly with a reason. Never drop silently.
- Idempotency keys must stay stable across retries.
- Validation rules are business rules: keep them visible and tested.
In this example
In
→ 3 records
Out
schema pass duplicates pass rejected 0
Fictional sample · not a client system