I think the biggest production risk in this design is concentrating the entire business workflow into a chain of Lambdas plus SQS without a clear orchestration, idempotency, and failure-recovery model. The API Lambda, parsing Lambda, scoring/LLM Lambda, and report generator Lambda all touch shared state in DynamoDB and S3, but I don’t see how duplicate SQS deliveries, partial failures, retries, or out-of-order events are handled. In production, that can easily create duplicate analyses, inconsistent job status, or reports generated from stale document versions. I’d also call out security and compliance risk around the Uploaded Financial Documents S3 bucket. For financial documents, I would expect explicit KMS encryption, strict bucket policies, malware scanning, PII redaction strategy, and auditability. Right now the architecture feels optimized for low cost, but the tradeoff is operational fragility and weak controls for sensitive data. If this were my design, I’d add Step Functions for workflow state, stronger DynamoDB conditional writes for idempotency, and a clearer security boundary around document storage and report access.