Large-scale Python systems design
Built as a modular platform with meaningful architectural scope rather than a single-purpose script.
Tyrell Towle, PhD • Flagship Engineering Portfolio Homepage
TowleVision
A large, modular, AI-powered Python automation platform built to orchestrate end-to-end multimodal workflows.
TowleVision is my flagship engineering project. It demonstrates modular Python architecture, applied AI orchestration, workflow automation, and product thinking across a complete pipeline from text intake through narration, captions, planning, image generation, final assembly, social clips, metadata, and publishing-oriented workflows.
Flagship output
The Snow Queen • long-form pipeline showcase
62,772
Python LOC
169
Python files
150
Non-
__init__ Python modules
11
Top-level core subsystems
34
Pipeline steps
14
Distinct AI models
15
AI implementations / integrations
End-to-end
Workflow from intake to publishing
What this project is designed to show
TowleVision is presented here as a serious flagship engineering project rather than a creative hobby. The outputs matter, but the deeper value is the system behind them: a large Python platform that coordinates dependent stages, integrates multiple AI capabilities, and turns raw input into polished deliverables.
Applied AI orchestration
Demonstrates how multiple AI-driven capabilities can be integrated into one coherent production workflow.
Automation with product intent
Focused on repeatability, usability, accessibility, and finished output quality instead of isolated experiments.
Python Automation
Applied AI
Workflow Systems
Internal Platforms
Scientific Software
Pharma-Adjacent Engineering
Flagship platform
The platform spans an end-to-end workflow from source text through finished long-form and short-form outputs. It is designed around orchestration, modularity, and controlled transformation across many dependent stages.
Core workflow coverage
- Text intake and project setup
- Narration and audio pipeline stages
- Caption generation and alignment
- Story planning and shot planning
- Image generation, review, and post-processing
- Video assembly and finalization
- Social clip generation, metadata, and publishing workflows
Engineering strengths demonstrated
- Configuration-driven workflow design
- Pipeline orchestration across dependent stages
- Multimodal AI integration and automation
- Accessibility-aware captioning and presentation
- Product thinking from raw input to finished deliverable
- A platform mindset that extends beyond one media use case
Why it matters beyond media
The transferable value here is not limited to video. TowleVision shows how I approach complex technical systems: define the workflow, modularize the stages, integrate AI where it adds leverage, maintain coherence across the pipeline, and keep the outputs usable and polished.
Additional technical materials available upon request
I maintain a private technical appendix for deeper review, including a polished pipeline diagram, a metrics summary, and a curated repo snapshot.
Visible proof of polish and implementation depth
These examples show that the platform produces polished, readable, format-aware outputs with attention to caption fidelity, presentation, and multi-format adaptation.
Caption style 1
Serif presentation with strong punctuation handling, clean line breaks, and visually integrated captions.
Caption style 2
Alternate caption styling for emphasis-heavy moments while preserving readability and polished presentation.
Caption fidelity across styles
Captioning is treated as a first-class system concern. TowleVision supports multiple polished caption styles while maintaining punctuation fidelity, readable line breaking, and presentation quality across different output contexts.
Format-aware social output handling
TowleVision is designed to extend beyond a single output format. This includes vertical short-form presentation, branding, caption handling, and layout choices that feel productized rather than improvised.
Selected projects
These projects are presented as evidence of real system execution, not just as creative samples. Together they show platform depth, output quality, and broader technical range.
Flagship showcase
The Snow Queen
The strongest flagship example of TowleVision as a complete system. It highlights end-to-end workflow execution across narration, captioning, story planning, image generation, pacing, and final assembly, while also carrying external recognition as a semifinalist.
Supporting output
Lord of the Flies Summary
A strong example of turning complex source material into structured, accessible output through narration, visuals, pacing, and caption-supported presentation.
Supporting output
Beowulf Summary
Demonstrates flexibility across different literary source material while preserving coherent delivery, visual pacing, and polished finished presentation.
Secondary technical project
AQI Informer
A Python application for monitoring local air quality through API-backed data retrieval, transformation, and visualization. Included here as evidence of practical, user-facing software development beyond TowleVision.
Secondary technical project
Mushroom detection Android app
Built from a workflow that involved annotating hundreds of mushroom images, training a detection model, and deploying an Android camera app that scans the environment and boxes mushrooms on-screen. This project demonstrates applied AI, computer vision, model deployment, and product-oriented thinking.
Contact
I use TowleVision Insight Lab as the centerpiece of my portfolio for remote Python, applied AI, automation, workflow systems, scientific software, and pharma-adjacent roles. The best way to evaluate my work is through this platform overview, the selected projects above, and my GitHub.
- Name: Tyrell Towle, PhD
- Website: TowleVision.com
- GitHub: github.com/tytanium12
- Email: doctortowle@gmail.com
Best-fit roles
Python automation, applied AI, workflow systems, internal platforms, scientific tooling, technical product development, and pharma-adjacent engineering work.
Based near Seattle, WA and especially interested in remote-friendly opportunities.