--- title: "Shawn Wilborne" description: "AI Builder Lid Vizion: practical AI implementation, connected tools and custom software." url: https://lidvizion.ai/author/shawn-wilborne/ site: Lid Vizion index: https://lidvizion.ai/llms.txt --- [Insights](https://lidvizion.ai/blogs/) · Author # Shawn Wilborne AI Builder [Let’s map your workflow](https://lidvizion.ai/contact/) · [All insights](https://lidvizion.ai/blogs/) ![Shawn Wilborne](https://rcicvvtcawdbfiuzwexb.supabase.co/storage/v1/object/public/webflow-media/webflow/media/cms/68abfe5aa543cfadd3538998/shawn.svg) ## Articles by Shawn Wilborne - [Building a Secure Expungement Automation Tool with Lid Vizion: OCR, Rules, Form Fill, and Secure Storage](https://lidvizion.ai/blog/secure-expungement-automation-ocr-rules-pdf-sharepoint/): How to ship an auditable expungement automation pipeline fast using Lid Vizion’s pre-built platform components: identity, event logging, rules, integrations,… - [Efficient Data Retrieval from Images: Using LlamaIndex, Mongo, and AWS](https://lidvizion.ai/blog/efficient-data-retrieval-from-images-using-llamaindex-mongo-and-aws/): This blog explains how image-centric RAG improves search accuracy by indexing image embeddings directly instead of relying on captions. Using CLIP or VoyageAI… - [Designing a Multi-Model Inference Routing System for Vision + LLM Workloads](https://lidvizion.ai/blog/designing-a-multi-model-inference-routing-system-for-vision-llm-workloads/): This blog explains how modern AI applications can intelligently route tasks across multiple models such as sending images to YOLOv8 and text to LLMs like GPT… - [Active Learning and Data Labeling Workflow for Computer Vision (From Pilot to Production)](https://lidvizion.ai/blog/active-learning-and-data-labeling-workflow-for-computer-vision/): Computer vision systems improve when you make labeling and retraining a routine, not a special event. Active learning is a set of tactics to label the - [Multi Tenant AI SaaS Architecture on AWS and MongoDB (A Practical Guide)](https://lidvizion.ai/blog/multi-tenant-ai-saas-architecture-aws-mongodb/): If you are turning an AI workflow into a SaaS product, the hardest parts are usually tenancy, security, and operations. This post outlines a practical multi… - [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… - [Cost Optimization for AI Document Pipelines on AWS (A Playbook)](https://lidvizion.ai/blog/cost-optimization-for-ai-document-pipelines-on-aws/): AI document pipelines can get expensive fast if they are built like prototypes. This post covers practical levers to reduce cost without sacrificing… - [Human in the Loop Document Review UI (Architecture on AWS and MongoDB)](https://lidvizion.ai/blog/human-in-the-loop-doc-review-ui-architecture-aws-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… - [Edge Computer Vision on Jetson with ONNX and TensorRT, Managed by AWS IoT](https://lidvizion.ai/blog/edge-computer-vision-aws-iot-onnx-tensorrt-jetson/): Edge deployment is often the right choice when bandwidth is limited, latency must be very low, or data cannot leave a site. This post outlines a practical… - [AWS Kinesis Video Streams and Real Time Computer Vision Architecture (What to Build First)](https://lidvizion.ai/blog/aws-kinesis-video-streams-and-realtime-cv-architecture/): Real time computer vision projects often fail for non ML reasons: stream reliability, buffering, latency, and alert routing. This post outlines a pra - [AWS Step Functions Patterns for Reliable Document Automation (Retries, Idempotency, and Audits)](https://lidvizion.ai/blog/step-functions-patterns-for-reliable-document-automation-on-aws/): Document automation is a long running workflow: ingest, OCR, validate, route, and generate outputs. AWS Step Functions is a strong orchestration - [MongoDB Atlas Vector Search RAG for Document Workflows on AWS (A Practical Blueprint)](https://lidvizion.ai/blog/mongodb-atlas-vector-search-rag-for-document-workflows-on-aws/): Retrieval augmented generation (RAG) is most valuable when it is attached to a real workflow: intake, search, extraction, and decision support. This post - [AWS Textract vs Open Source OCR for Production Doc AI (Accuracy, Cost, and Architecture)](https://lidvizion.ai/blog/aws-textract-vs-open-source-ocr-for-production-doc-ai/): Who this is for If you are building document automation that has to work in production, not just in a notebook, this guide compares AWS Textract with l - [Monitoring, Logging, and Analytics for Vision Systems](https://lidvizion.ai/blog/monitoring-logging-and-analytics-for-vision-systems/): Modern computer-vision workflows often involve multiple specialized agents (image classifiers, annotation tools, QA reviewers, billing trackers) working… - [AI-Enhanced Apps with React and AWS for Vision](https://lidvizion.ai/blog/ai-enhanced-apps-with-react-and-aws-for-vision/): Learn how to use and train the three new state-of-the-art RF-DETR object detection model sizes. - [From Pixels to People: The Evolution of 3D Avatars from Research Labs to Your Browser](https://lidvizion.ai/blog/from-pixels-to-people-the-evolution-of-3davatars-from-research-labs-to-your-browser/): Learn how to use and train the three new state-of-the-art RF-DETR object detection model sizes. - [Scalable MongoDB Design for Vision Metadata](https://lidvizion.ai/blog/scalable-mongodb-design-for-vision-metadata/): Learn MongoDB schema patterns, sharding, and vector storage strategies to manage large-scale computer vision metadata cost-effectively. - [Real-Time Computer Vision Data and Event-Driven Systems](https://lidvizion.ai/blog/real-time-computer-vision-data-andevent-driven-systems/): Learn how to build real-time computer vision systems with event-driven pipelines, WebSockets, MongoDB change streams, and live annotation tools. - [DevOps and Deployment Pipelines for CV Applications](https://lidvizion.ai/blog/devops-and-deployment-pipelines-for-cv-applications/): This blog outlines how to build a DevOps CI/CD pipeline for deploying computer vision (CV) models using GitHub Actions and AWS. It walks through every… - [File and Media Management in Vision Workflows](https://lidvizion.ai/blog/file-and-media-management-in-vision-workflows/): Learn key SEO expert interview questions to assess skills, strategy, and tools for hiring the right candidate. - [Real-Time Sports Analytics: From Pickleball Ball Tracking to Scalable CV Pipelines](https://lidvizion.ai/blog/real-time-sports-analytics-from-pickleball-ball-tracking-to-scalable-cv-pipelines/): Modern computer-vision workflows often involve multiple specialized agents (image classifiers, annotation tools, QA reviewers, billing trackers) working… - [Multi-Agent Orchestration for Vision Systems (LangChain + AWS)](https://lidvizion.ai/blog/multi-agent-orchestration-for-vision-systems-langchain-aws/): Modern computer-vision workflows often involve multiple specialized agents (image classifiers, annotation tools, QA reviewers, billing trackers) working… - [Introduction: Semantic Video Retrieval and RAG](https://lidvizion.ai/blog/introduction-semantic-video-retrieval-and-rag/): This blog explains how Retrieval-Augmented Generation (RAG) can transform traditional video search by enabling users to query massive video libraries through… Bring the messy version ## One workflow. A better way to run it. Show us the tools, the handoffs, and the part that keeps slowing your team down. [Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)