Founder & Lead Engineer
DotTrim — AI Video Analysis Platform
A desktop-first AI footage analysis product for videographers, combining local video processing, AI-assisted analysis, NLE export workflows, telemetry, and hybrid local/cloud architecture.
Problem
Video editors spend a significant amount of time manually scrubbing through footage, identifying usable segments, and organizing clips. Pure cloud solutions are often impractical due to file sizes, privacy concerns, and unreliable connectivity.
Solution
Built a hybrid local-first system that performs video processing and indexing on the user’s machine, with optional cloud-based AI processing for heavier analysis. The system generates structured metadata that editors can use to quickly assemble rough cuts and explore their footage.
Architecture & Approach
Electron and React desktop application backed by local Python services for video processing, using FFmpeg and OpenCV for media analysis. Local indexing is stored in SQLite, with PostgreSQL used for cloud-side services and Node.js microservices handling supporting workflows.
Key Tradeoffs
- Local-first processing increases client complexity but avoids large uploads and latency
- Hybrid cloud support adds architectural overhead but enables more advanced AI analysis
- Balancing performance with cross-platform desktop support required careful resource management
What I Learned
- Desktop applications introduce challenges not present in web apps, including packaging, updates, and OS-specific behavior
- Editor workflows vary significantly; early user feedback influenced both UX and system design
- AI pipelines need robust fallbacks when models fail or produce low-confidence results