--- title: "Human in the Loop Document Review UI (Architecture on AWS and MongoDB)" description: "Fully automated document extraction is rare in real operations. The difference between a pilot and a production system is usually a clean human in the loop layer that ha" url: https://lidvizion.ai/blog/human-in-the-loop-doc-review-ui-architecture-aws-mongodb/ site: Lid Vizion index: https://lidvizion.ai/llms.txt --- [Insights](https://lidvizion.ai/blogs/) Integration Playbooks 5 min read 2025-11-03 # Human in the Loop Document Review UI (Architecture on AWS and MongoDB) Fully automated document extraction is rare in real operations. The difference between a pilot and a production system is usually a clean human in the loop layer that ha By [Shawn Wilborne](https://lidvizion.ai/author/shawn-wilborne/) [Integration Playbooks](https://lidvizion.ai/blog-categories/integration-playbooks/) Tags: [OCR](https://lidvizion.ai/tags/ocr/), [MongoDB](https://lidvizion.ai/tags/mongodb/), [AWS Step Functions](https://lidvizion.ai/tags/aws-step-functions/), [AWS](https://lidvizion.ai/tags/aws/) ![Hero image for: Human in the Loop Document Review UI (Architecture on AWS and MongoDB)](https://rcicvvtcawdbfiuzwexb.supabase.co/storage/v1/object/public/webflow-media/webflow/media/cms/69927ccd1fcb2cff2feb05c3/68cdf6bb2acf72bfc5602273-monitoring-logging-and-analytics-for-vision-systems.png) ## Key Takeaways - Human in the loop review is the fastest way to reach production reliability. - Keep the UI focused on exceptions, not full document editing. - Store provenance and audit logs so you can defend every value. - Use validation gates to control review workload. Fully automated document extraction is rare in real operations. The difference between a pilot and a production system is usually a clean **human in the loop** layer that handles exceptions quickly. This post describes a pragmatic review UI architecture using AWS for storage and compute, and MongoDB for workflow state. ## Why human in the loop is a feature, not a failure Human review is what lets you: - Ship sooner, before you have perfect training data - Control risk on sensitive fields - Improve continuously through feedback loops ## What the review UI should actually do A useful review UI is focused: - Show the original page and the extracted fields side by side - Highlight citations and bounding boxes - Let reviewers correct fields with minimal clicks - Capture reason codes for failures - Support queues and assignments ## Reference architecture ### Storage - Raw docs in S3 - Normalized page images in S3 - Evidence and redlines in S3 S3 reference: [https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html) ### API and workflow - Step Functions for orchestration - API service for task assignment and updates References: - Step Functions: [https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html) - AWS Lambda: [https://docs.aws.amazon.com/lambda/latest/dg/welcome.html](https://docs.aws.amazon.com/lambda/latest/dg/welcome.html) ### MongoDB collections - `review_tasks`: status, assignedTo, dueAt - `extracted_fields`: field values, confidence, citations - `review_actions`: diffs, timestamps, reviewer ID - `audit_log`: append only MongoDB change streams are useful to push UI updates: - [https://www.mongodb.com/docs/manual/changeStreams/](https://www.mongodb.com/docs/manual/changeStreams/) ## Quality gates and confidence thresholds Two simple gates reduce review volume: - Only send to review when a required field is missing or below confidence threshold - Auto approve when validation rules pass ## Security and access control Review systems often contain PII. Baseline controls: - Strong authentication and RBAC - Per tenant authorization checks - Encrypted storage and short lived signed URLs References: - OWASP ASVS: [https://owasp.org/www-project-application-security-verification-standard/](https://owasp.org/www-project-application-security-verification-standard/) - AWS KMS: [https://docs.aws.amazon.com/kms/latest/developerguide/overview.html](https://docs.aws.amazon.com/kms/latest/developerguide/overview.html) zion OCR demo: [https://ocr.lidvizion.ai/](https://ocr.lidvizion.ai/) ## FAQs **Q: How many reviewers do we need?** Start by measuring the exception rate. A good system reduces review volume over time through validation and model improvements. **Q: Can we use reviewers to improve models?** Yes. Store corrected values with citations and use them as labeled data for retraining or rule refinement. **Q: How do we prevent reviewers from seeing data they should not?** Enforce tenant checks in the API, and limit S3 signed URL scope and lifetime. Internal reference: - Lid Vi **Q: What should we link to internally?** A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links. [PreviousCost Optimization for AI Document Pipelines on AWS (A Playbook)](https://lidvizion.ai/blog/cost-optimization-for-ai-document-pipelines-on-aws/) · [NextEdge Computer Vision on Jetson with ONNX and TensorRT, Managed by AWS IoT](https://lidvizion.ai/blog/edge-computer-vision-aws-iot-onnx-tensorrt-jetson/) Keep reading ## Related insights. - [AI Document Analysis for Contract & Form Review: Real Estate + B2B Workflow Demo](https://lidvizion.ai/blog/ai-document-analysis-contract-form-review-real-estate-b2b/): See how Lid Vizion’s Document Intelligence workflow drops into your app to review forms, extract fields, and flag compliance issues—ideal for real estate brokerages and any document-heavy team. - [Audit Logging and Provenance for AI Workflows (AWS and MongoDB Patterns)](https://lidvizion.ai/blog/audit-logging-and-provenance-for-ai-workflows-aws-mongodb/): If an AI system affects legal, financial, or compliance outcomes, you need to answer: what happened, when, why, and who approved it. This post outlines audit logging a ## Where does your workflow get stuck? [Discuss the workflow](https://lidvizion.ai/contact/) · [Book a call](https://calendar.app.google/bB26LdA9e2gmGFp69)