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I'm currently designing an AI-assisted guest-flow intelligence system for a confidential large-scale immersive entertainment venue.
The venue already has an extensive hardwired camera network. The goal is not to replace it. It's to turn selected camera feeds into a new operational intelligence layer.
System Architecture
The proposed system uses a dedicated onsite NVIDIA-powered AI server to:
Detect guests anonymously without facial recognition
Associate guests into temporary operational groups
Display numbered group markers on a live floor map
Measure dwell time, occupancy, pacing, and group gaps
Identify developing slowdowns, compression, and bottlenecks
Monitor participating camera feeds
Generate end-of-shift operational reports
Two Complementary Interfaces
Live Map Overview
A command-center display showing where anonymous guest groups are located, how many people each marker represents, and where congestion is developing.
Route Operations Dashboard
A simplified dashboard built for fast decisions, tablets, and mobile viewing. It shows the active problem, its severity, and the recommended response.
The Hard Engineering Problem
The system is designed around the venue's existing cameras and DVRs, using controlled read-only video streams and local processing. All operational video analysis and event data remain onsite.
The hardest part is not creating a beautiful dashboard. It is engineering reliable tracking through darkness, fog, strobes, costumes, occlusion, and non-overlapping camera views while honestly displaying uncertainty when a handoff cannot be confirmed.
Current Work
Technical architecture
Computer-vision validation planning
Onsite server design (custom NVIDIA build)
Camera and zone mapping
Route-aware group handoff logic
Event database design
Live dashboard prototyping
Acceptance testing and deployment planning
The Approach
This is the kind of project I enjoy most: taking infrastructure a business already owns and transforming it into a system that helps the operation see more, respond faster, and make better decisions.
Existing surveillance becomes operational intelligence.
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AI Guest-Flow Intelligence System Design
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You don’t have a marketing problem. You have a 3-second problem.
The first 3 seconds decide everything.
People don't read your pitch or landing page first.
They stop because of a hook, a stunning visual, a bold statement, or a pattern interrupt.
If you fail there, the rest of your funnel might as well not exist.
In business, we obsess over:
Open rates.
Click-through rates.
Conversion rates.
But the real conversion starts with attention.
That's why high-performing brands focus on:
✅ A strong first line.
✅ A curiosity gap.
✅ A relatable pain point.
✅ A surprising insight.
✅ A visual that makes people pause (like this video 🐶).
Think of it like this:
Bad hook: "We offer professional digital marketing services."
Better hook: "90% of your content dies before the third second."
Same topic. Completely different outcome.
Attention is the new distribution.
And the brands that master the first 3 seconds will outperform competitors with 10x the budget.
So before you launch your next campaign, ask yourself:
Would you stop scrolling for this?
Need more inbound leads, brand visibility, and seamless marketing workflows that actually convert?
I help founders and businesses build automated marketing engines and digital strategies that attract paying clients, not just views.
📩 Check out my portfolio here to see how RVW Systems can help you build a marketing pipeline that sells while you sleep. Send me a message and let's get to work!
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Content creation shouldn't feel like a chaotic, unscalable scramble every time you need a new campaign asset.
Most businesses treat creative work as a one-off art project. I treat it as a system.
Alongside my automation architecture, I build AI-powered visual content systems for YouTube, brands, and digital products. My advantage? I approach creative production as both a creator and a systems engineer.
I don’t just make a video, thumbnail, or logo in isolation. I design a repeatable production process that actually scales.
Here is what that looks like in practice:
🎬 Video Editing & Storytelling: Built for pacing, viewer retention, and platform-ready execution.
🤖 AI Art Direction: Using AI as a rapid visual prototyping tool for brand assets, not just a shortcut.
📊 Complex Infographics: Turning messy operational workflows into clear, tangible graphics.
🎨 Brand Identity: Designing visual systems that stay consistent across every single touchpoint.
If your team needs stronger content without losing consistency or drowning in manual edits, take a look at my complete creative portfolio and service lineup over on Contra.
Let's build your visual content engine. Link is in the comments! 👇
#VideoProduction #AI #ContentStrategy #SystemsEngineering #BrandDesign #Contra #RVWSystems
Check it out (https://contra.com/p/eAM73cBk-video-production-you-tube-strategy-and-ai-creative)
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Adapting Dungeon Crawler Carl into a Multi-Episode AI Video Series
I am currently producing a full-length, multi-episode video adaptation of the bestselling LitRPG series Dungeon Crawler Carl by Matt Dinniman, built entirely using AI video generation tools.
This is not a proof of concept or a short demo reel. It is a long-form narrative production where character consistency across every scene, every episode, and every camera angle is the central technical challenge.
The release is still roughly two years out. What I can share now is the production methodology, the pipeline architecture, and a sample of the visual quality the system produces.
https://media.contra.com/video/upload/fl_progressive/w_1600/h0sumzd0faydmt1eksbj.mp4
Sample output from the Kling AI 3.0 pipeline. Audio omitted due to proprietary content.
The Core Problem: Character Consistency at Scale
Single-shot AI video is easy. Maintaining a character's face, build, wardrobe, and physical presence across dozens of scenes, camera angles, and lighting conditions over multiple episodes is where most AI video projects fall apart.
I solved this using a combination of strict reference management, programmatic prompting, and a locked-string discipline that treats every character description as an immutable asset.
Production Architecture
The pipeline operates as an interconnected automation ecosystem, not a single tool.
Pre-Production — Midjourney / Flux
Every character begins as a locked "Golden Sample": a master reference sheet with four clean angles (front, back, side profile, and 3/4 view). These feed directly into Kling's Element Binding system so the AI never has to guess an unseen angle. This is the single most important step for preventing "face melting" across shots.
Rendering — Kling AI 3.0
Kling is the core rendering engine. Version 3.0 supports multi-shot generation (up to 15 seconds with 6 defined camera cuts per block), native audio synchronization, and Subject Element Binding for character consistency. I operate it like a physical soundstage, not a chatbot.
Audio — ElevenLabs
Highly customized emotional voice cloning and clean dialogue tracks. Kling's native lip-sync maps directly to external audio files.
Orchestration — Make.com (http://Make.com) + Airtable
Because rendering 1080p clips takes several minutes per shot, the pipeline uses asynchronous webhooks to manage API handoffs between Airtable databases and Kling's servers. Airtable acts as the master database for shot lists, character lore, and automated tracking matrices.
Post-Production — CapCut + Topaz AI
Micro-batched 4-to-5-second clips are stitched together in CapCut, with timeline pacing, universal color LUTs, and final 4K upscaling through Topaz AI. Raw assets (15MB to 30MB per clip) are mirrored locally via desktop sync to prevent cloud-fetching latency during editing.
Prompting as Directing
The quality gap between amateur AI video and production-grade output comes down to how you write the prompt. I treat every prompt as a visual environmental tracking matrix.
Time-Coded Storyboarding
Kling responds best to sequential, time-coded directives. Transitions are forced using hard time markers (e.g., "Shot 1 (0-4s): Wide establishing shot...") rather than narrative paragraphs.
Motivated Camera Moves
Every camera movement gets a strict physical path: a slow dolly push, a rack focus, a locked tracking shot. Unmotivated camera movement is the primary cause of character mutation in AI video.
The Locked String Discipline
When describing a character's state or gear, the exact same string of words is used in every single generation. Changing "rugged combat gear" to "dirty armor" forces the AI to recalculate the asset from scratch, causing visual drift. Consistency is a vocabulary problem as much as a technical one.
Kinetic Anchors
To break the smooth, weightless "AI look," I introduce natural forces into every scene: wind, dust, friction, fabric weight. This forces the engine to calculate environmental resistance and real-world physics, producing motion that feels grounded.
Why This Matters
AI video production is moving fast, but most of what exists today is short-form, single-character, single-scene content. Building a multi-episode narrative with consistent characters, coherent world-building, and production-grade visual quality requires the same discipline as traditional film production, just with a fundamentally different toolchain.
The methodology I have built for this project applies directly to any long-form AI video production: brand series, product narratives, educational content, or entertainment.
This project is in active production. More will be shared as the release approaches.
Check it out (https://contra.com/p/kmmRerLW-ai-video-production-bringing-a-bestselling-book-to-life)
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ClearMeds: Turning Medication Changes Into Clear, Clinician-Verified Patient Instructions
Some of the best product ideas do not begin in a brainstorming session.
They begin when someone you love is struggling.
My wife deals with ongoing medical issues, and her medications can change frequently. After an appointment, it can become difficult to remember:
Which medication changed
Which medication is new
What should be stopped
What is taken only as needed
What should be taken in the morning or at bedtime
During one of her visits, I observed the medication-reconciliation process and spoke with the doctor and RN about the challenges patients and caregivers can face after leaving the office.
The medical information was already being documented—but the final instructions were not always presented in a way that was easy for every patient to understand.
That observation became ClearMeds, my submission for the Base44 × Contra Give It a Glow Challenge.
ClearMeds is an independent local-business concept designed with Schroer Medical Clinic in Lockesburg, Arkansas, and patients like my wife, in mind.
The workflow is simple:
Capture → Extract → Verify → Approve → Print
A clinic staff member photographs the existing medication-reconciliation sheet. ClearMeds organizes the printed medications and handwritten changes into a structured review workspace.
The clinician then reviews every medication beside the original source information.
They can:
✅ Confirm accurate information
✏️ Correct unclear instructions
⚠️ Resolve conflicting doses
🛑 Stop the process when information is incomplete
🔐 Complete additional safeguards for high-risk medications
📋 Record verified exclusions and approval history
Only after every included medication has been reviewed can the system generate a simple patient-friendly summary organized by:
What Changed Today
Morning
Midday
Evening
Bedtime
As Needed
Stop Taking
Because this involves medication information, safety had to guide every design decision.
ClearMeds does not diagnose, prescribe, select medications, calculate dosages, or replace a healthcare provider, pharmacist, prescription label, EHR, medication order, or official medical record.
The AI does not make the decision.
It organizes the documented information, identifies uncertainty, and helps the clinician communicate the final instructions more clearly.
AI organizes. Clinicians decide.
The demonstration uses completely fictional patient data and a synthetic medication document. ClearMeds is an independent contest prototype and is not affiliated with, endorsed by, approved by, or currently deployed by Schroer Medical Clinic.
The goal is not to replace the existing clinical process.
It is to add one helpful final step so more patients and caregivers can leave the office understanding exactly what changed.
For patients like my wife, that small step could make a meaningful difference.
Built with Base44 for the Contra #base44giveitaglowchallenge.
🔗 Live app: https://clear-meds-flow.base44.app/demo
🎥 Full walkthrough: Attached to this post
💬 I would especially value feedback from healthcare providers, nurses, pharmacists, caregivers, and anyone who has struggled to manage changing medications.
#base44giveitaglowchallenge #Base44 #Contra #PatientEducation #HealthcareInnovation #HumanInTheLoop #ResponsibleAI #AIForGood #LocalBusiness
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A roofing company was spending $70,000 a year on a 4-person manual inventory and procurement operation.
I replaced it with a zero-touch pipeline. No human data entry. No spreadsheet handoffs. No "did we order that?" Slack messages at 9pm.
The system runs on Airtable, Python, and OpenAI. It took 3 weeks to build. It paid for itself in the first month.
Here's what I've learned after 20+ years of operations work and building these systems across hospitality, e-commerce, SaaS, and construction:
The expensive problem is almost never the one you think it is.
It's not the software. It's the 6 spreadsheets nobody wants to touch. It's the workaround that became company policy. It's the process that only works because one person memorized it.
I build systems that replace all of that: the CRM, the automation, the AI agents, the dashboards, the content pipelines. One architect, one connected system, everything built to work together from day one.
Recent builds:
Recovered 20 hours/week for a multi-venue hospitality operation
Unified 5 e-commerce storefronts into one automated command center
Built an AI system that replaced 8 improv actors per venue per night (yes, really)
Turned a hiring round into a one-button AI content pipeline
What's the one manual process in your business that you know is costing you the most time, but you haven't fixed yet?
#BusinessAutomation #AI #Operations #SystemsArchitecture #Airtable #Python #WorkflowAutomation #NoCode #ECommerce +1
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Video Production, YouTube Strategy & AI Creative
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Logo Design & Brand Video Production
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Building an AI-powered affiliate publishing engine from the ground up.
I am currently working on a full automation system for NodeRidge, designed to turn product and software research into structured, review-ready WordPress content with human approval built into the workflow.
The goal is simple: remove the repetitive work from affiliate publishing while keeping quality control, visibility, and editorial review in place.
This build connects:
🗄️Airtable as the central operations hub
⚡Make.com (http://Make.com) for workflow automation
🧠OpenAI for content generation
📝WordPress for draft publishing
📊Google Search Console for rank and performance tracking
What I like most about this project is that it is not just "AI writing content." It is a real backend publishing engine with structured data, review gates, sync monitoring, and operational visibility.
The system is already handling prompt runs, WordPress draft syncing, affiliate link registry logic, and QA tracking. My next focus is scaling the Airtable architecture to handle higher volume.
This is the kind of build I enjoy most: connecting messy manual workflows into a clean system that can actually scale.
What are the biggest operational bottlenecks you're currently dealing with in your own workflows?
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Stop building science experiments and start building scalable, autonomous operational systems.
I just published a detailed look under the hood at my most sophisticated AI integration yet: The Hierarchical Multi-Agent Business System.
Most founders use ChatGPT to write emails. I build AI agent teams to source leads, design technical architectures, manage client-facing dashboards, and automate complex content pipelines—all with zero human input for everything except final approval.
Take a look at the attached infographic to see how this recursive, self-optimizing architecture functions. Here is the operational impact of what I build:
Departmental Specialization:
I don't use one "smart" agent. I build a crew of highly specialized agents (Lead Gen, Content, DevOps) overseen by a Manager Agent to ensure quality and prevent hallucinations.
The Human-in-the-Loop Safeguard:
The entire workflow is designed to be fully autonomous until final quality control and strategic approval. You get the scale of AI with the certainty of a human final check.
Tool Agnosticism:
My builds are logic-first. We use the best tool for the task, whether that's CrewAI, LangGraph, Supabase, Cloudflare, OpenAI, or a custom API.
If you are a founder or operator ready to turn your standard business workflows into high-performance, autonomous engines, check out the full case study.
https://contra.com/s/2rFLgNAU-custom-ai-agent-for-your-business-workflow
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20 Hours/Week Recovered: Rebuilding Multi-Venue Operations
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How AI Replaced 8 Improv Actors Per Venue Per Night
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5 Storefronts, One System: An E-Commerce Operations Hub
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Building Tether — From Noisy Telemetry to Deterministic Operations
Role: Lead Architect & Full-Stack Developer
Tech Stack: React, Cloudflare (Pages, Workers, R2, Zero Trust), Google Cloud Platform (Cloud Run, Cloud Storage), Python, FastAPI, Scikit-Learn.
The Challenge: The Hospitality Data Gap
Modern hospitality operators are drowning in data but starving for actionable intelligence. A restaurant's two most critical systems—the Point of Sale (revenue) and the scheduling platform (labor)—operate in complete isolation. Because these systems do not dynamically communicate, managers are forced to make high-stakes labor cuts on the fly based on delayed reporting and gut feeling.
This disconnect results in thousands of dollars of weekly margin bleed. The challenge was clear: build a system that bridges these fragmented APIs, normalizes the data, and provides real-time operational certainty.
The Solution & The Product Pivot
I engineered Tether to be an AI-native operational layer for restaurant management. However, the true breakthrough of this project wasn't just technical—it was architectural.
Initially, I designed Tether as a "Live Data Prediction Tool" that used active telemetry to drive real-time floor decisions. Through testing and auditing the data streams, I identified a critical UX flaw: live data is inherently noisy and reactive. To solve this, I executed a complete priority inversion, refactoring the application state to a "Schedule-First" philosophy.
Instead of chasing live data, Tether now ingests historical data to generate a deterministic, highly optimized 14-day schedule baseline. The machine learning models were strategically demoted from "decision makers" to "real-time guardrails." Once the floor opens, Tether acts as a safety net, validating execution against the baseline and alerting managers to profit leaks before they compound.
Technical Execution: A Masterclass in Edge ML
To ensure security, scale, and sub-100ms latency, I architected Tether as a zero-backend Single Page Application (SPA) driven by serverless microservices.
Edge Infrastructure & Security: The frontend is deployed via Cloudflare Pages and secured behind a Cloudflare Zero Trust perimeter, requiring One-Time PIN (OTP) authentication for operator access.
Data Normalization: I developed Cloudflare Worker proxies to securely handle OAuth handshakes, ingest data from POS systems (Square, Toast) and labor platforms (7shifts), and normalize the varied streams into a unified, sanitized client schema.
Autonomous ML Pipeline: I engineered a fully autonomous, serverless retraining loop hosted on Google Cloud Run. Every Tuesday at 3:00 AM UTC, the pipeline wakes up, pulls historical telemetry from Cloudflare R2, and retrains the primary Approval and Labor-to-Sales (LTS) models (using Ridge and Logistic Regression).
Strict Data Contracts: The ML pipeline strictly enforces a 63-feature data contract. It validates baseline accuracy and ensures zero NaNs before allowing any model to pass into production, guaranteeing operational stability.
Highly Optimized Model Distribution: Fresh model weights are served to the browser via a Dockerized FastAPI microservice (kept aggressively lean at ~500MB) and distributed globally through Google Cloud Storage (GCS).
The Business Impact
Tether replaces the anxiety of restaurant management with mathematical certainty.
By automating the schedule generation and monitoring real-time Labor-to-Sales (LTS) velocity, Tether catches margin bleed live—such as a sudden drop in patio sales pace due to weather. It translates complex ML predictions into simple, actionable alerts (e.g., "Trim one support role. Protects $190 margin.").
The result is protected daily profit margins, guaranteed labor compliance, and management teams empowered to run their floors with absolute confidence.
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Tether is a custom middleware solution engineered to eradicate one of hospitality's most expensive bottlenecks: the gap between legacy POS systems and labor scheduling. By leveraging Make.com (http://Make.com) for high-frequency API polling, Tether automatically cross-references live sales data with time punches and maps it into a unified, real-time dashboard. Zero manual reporting, zero data silos—just instant visibility into labor-to-sales ratios.
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How a Roofing Company Eliminated $70K/Year in Manual Operations
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From Hiring Round to One Button: An AI Content Pipeline
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Architected an autonomous AI pipeline that ingests product links, conducts SEO research, and generates publish-ready affiliate reviews with zero manual writing.
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Business Automation Specialist
I design and build fully automated business systems that eliminate manual work, reduce errors, and help companies scale faster without increasing overhead.
From lead capture to final reporting, I connect your tools into one seamless workflow using automation platforms, APIs, and custom logic.
What I Do
I turn scattered processes into clean, reliable systems:
Capture and route leads instantly
Automate follow-ups via email and SMS
Build and manage CRM pipelines
Sync data across platforms in real-time
Create dashboards for clear, actionable insights
Systems I Build
Lead Capture → CRM → Notifications
Sales Pipelines with automated stage progression
Follow-up engines that engage prospects automatically
End-to-end workflows from first click to closed deal
Real-time reporting dashboards
The Result
No more manual data entry
No more missed leads
No more inconsistent follow-ups
Faster response times
Scalable, reliable operations
My Approach
Every system I build is designed to be:
Simple – easy to use and maintain
Reliable – runs without constant oversight
Scalable – grows with your business
Bottom Line
I don’t just automate tasks…
I build systems that run your business for you.
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