Seat Level Automation
A reference-locked automation pipeline designed for a major sports ticketing platform serving millions of fans.
The system generates accurate seat-level venue imagery using AI as a production layer — not a generative layer. This distinction is a core principle of production-grade AI deployment: establish ground truth first, apply generation only within verified constraints.
Real seat-view photography and venue metadata lock geometry and perspective; AI handles reconstruction and enhancement only, preserving spatial accuracy across every output. Photogrammetry and gaussian splatting anchor the pipeline in physical measurement.
The Methodology
"Establish ground truth first, apply generation only within verified constraints."
This is the core principle. In production-grade AI deployment, unconstrained generation produces outputs that look correct but aren't. Reference-locking solves this: real photography provides the geometry, AI provides the scale.
Pipeline Architecture
Reference Capture
Real seat-view photography establishes ground truth. Every angle, every section, documented.
Geometry Lock
Photogrammetry and gaussian splatting extract 3D spatial data. Perspective is fixed, not interpreted.
AI Enhancement
Generation operates within locked constraints. Reconstruction and enhancement only. No hallucination.
Production Output
Production-ready seat-view images. Consistent quality. Suitable for automated A/B testing at scale.
Pipeline Output
Real seat-view photography and venue-specific metadata used to lock geometry and perspective before any generative step is applied.
AI used selectively for reconstruction and enhancement while preserving spatial accuracy and camera position.
Consistent, production-ready seat-view images suitable for automated A/B testing and large-scale deployment.
Final Output · Production Ready Technical Stack
Capture
Photogrammetry · Reference photography
3D Processing
Gaussian splatting · Point cloud extraction
Generation
Constrained diffusion · Reference-locked inpainting
Validation
Automated QA · Spatial accuracy verification
Engagement Context
Developed during a multi-week engagement with a major sports ticketing platform. The system was designed in response to a real production challenge: generating accurate seat-level venue imagery at scale for a platform serving millions of sports fans.
Methodology validated. The pipeline architecture is transferable to any domain where AI generation must be constrained by spatial or factual ground truth — real estate imaging, e-commerce product visualization, architectural rendering.