Michael AdewaleMichael AdewaleStaff Product Designer
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02Internal Systems / AI Automation / DevOps

Designing the headless operational engine and AI data pipelines that run MuviDB as a solo founder.

Designing an interface is easy compared to keeping a 41,000-title database running alone. MuviDB Studio is the internal operating system behind the curtain: local video OCR credit harvesting, Telegram-driven headless moderation, deterministic deduplication, and automated social publishing.

My RoleFounder / Systems & Design Engineer
Team
MePMFEBESolo founder & engineer
Timeline2025–Present · Live in Production
Executive Shortcut / 90s Read

Five key questions on problem, decisions, and outcomes.

Designed for hiring managers, engineering leads, and evaluators who need the essentials in under two minutes.

01
The Problem Framing

What was the friction?

A solo founder cannot manually maintain a 41,000-record media database.

Building MuviDB meant indexing thousands of films across multiple eras, but manual catalogue enrichment, credit verification, and social marketing would require a full operations team I couldn't afford.

Standard startup admin dashboards require substantial ongoing engineering maintenance, distracting from consumer product iterations.

02
Discovery & Signals

What is the evidence?

Repetitive editorial patterns and unstructured credit data were capping growth.

Observing early operations revealed high-friction bottlenecks:

  • Closing credits on YouTube films are trapped inside video pixels, not text metadata.
  • Multi-source scraping created thousands of fragmented duplicate records that corrupted actor career totals.
  • Manual social media creation was completely patterned: film poster + rating + director + streaming link.
  • Desktop admin portals forced the founder to sit at a laptop to approve urgent community edits.
03
Design & Systems Craft

What did Michael do?

Engineered headless Telegram ops, local OCR pipelines, and deterministic merge tooling.

Transformed operational bottlenecks into automated, human-in-the-loop systems:

Headless Ops via Telegram BotRouted alerts and community edits to Telegram inline buttons, eliminating the need to build a complex desktop admin web app.
Local Computer Vision & OCR HarvestingBuilt a Python/Tesseract pipeline to extract actor names from video closing frames with human-assisted verification.
Deterministic Deduplication & MergeEngineered fingerprint comparison algorithms and relational database merge RPCs to reconcile duplicate titles safely.
Social Studio Automated TemplatingCreated an automated template composer that formats, validates, and queues social content directly from database records.
04
Results & Longevity

What was the outcome?

Single-operator sustainability across 41,000+ films with zero ongoing admin UI overhead.

Reduced administrative time from 20+ hours a week of manual data entry to a few daily taps on Telegram.

Cut infrastructure and bandwidth costs by >80% through Cloudflare R2 and Supabase storage separation.

Demonstrated that design engineering extends beyond public screens into operating model sustainability.

1Solo operator maintaining the platform
41,000+Films enriched and deduplicated
>80%Bandwidth cost reduction via storage split
0Dedicated desktop admin screens to maintain
05
Intellectual Honesty

What does this leave out?

What this leaves out (intellectual honesty).

OCR extraction requires clean source video; low-resolution legacy VHS transfers still require manual human transcription.

Automating social publishing eliminated a contracted marketing role—a commercially necessary trade-off that is not framed as an unalloyed win.

The Telegram bot is private and scoped to founder operations, not designed for large multi-tenant moderation teams.

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