Scalable digital-twin production
The business task was not to create one striking 3D asset but to organize repeatable production for a large model portfolio. Every result had to match its source data, a common standard and the requirements of the target system.
- Status
- In progress
aircode’s role
aircode designed the process from source-data analysis and modelling through automated validation, optimization and export.
Context
Mixed source materials, manual operations and a large volume of work created a risk of errors and inconsistency. The process needed formal stages, verifiable quality and outputs ready for downstream use without manual rework.
The task
- scale production across a large portfolio of spatial models
- normalize mixed source materials
- establish one standard for structure and detail
- automate repetitive operations and validation
- reduce geometric and structural errors
- optimize models for the target system
Solution
Production is divided into verifiable stages with common input and output requirements. Automation prepares scene structure, detects common errors and produces export-ready results.
Quality control is built into the process: deviations are found before a model moves forward, and the status of every asset remains visible to the team.
Visual system
Statuses, checks and issues live in one working environment. The team sees portfolio progress, reasons for rework and each asset’s export readiness.
Production automation
- project structure preparation
- geometry and material checks
- detection of common errors
- level-of-detail control
- optimization and batch export
How it works
- Source-data intake and normalization
- Base structure preparation
- Spatial model production
- Automated validation
- Expert quality control
- Optimization and export
Outcome
Manual work became a managed production pipeline. One standard and built-in checks make it possible to scale the portfolio while preserving predictable output quality.