Prepare assets & scenes
Organize source files, identifiers, and scene inputs.
Collect a representative subset of the studio’s assets first. Establish IDs, version conventions, source paths, and which materials can enter the first run.
Game AI production workflow
From your game assets.
To a repeatable AI pipeline.
Explore an illustrative asset-retrieval project: the inputs, engineering work, and deliverables at each stage.
01 / Project inputs
Your existing resources.
Start with the assets, code, and environment already in your project.
3D assets + source paths
Images + reference tasks
Model code + requirements
02 / Connected engineering
Data. Training. Integration.
Render controlled samples. Organize the data. Adapt the model. Connect inference.
SCOPED TO YOUR PROJECT + ENVIRONMENT
03 / Project delivery
Ready for your next step.
Clear outputs, with the materials needed to run and update the workflow.
Models + configurations
Datasets + mappings
Interfaces + documentation
Service workflow illustration · The scope and outputs are defined for each project.
Follow the work.
From source to studio tool.
Scroll through the stages. Each one connects a concrete input with a reviewable output.
01
Prepare assets & scenes
Organize source files, identifiers, and scene inputs.
02
Render controlled samples
Generate agreed views and associated scene information.
03
Process & organize data
Connect samples with their sources and task requirements.
04
Train or adapt the model
Run experiments around a defined objective.
05
Compare results
Review candidates, failure cases, and operating requirements.
06
Deploy inference
Connect the model and establish an update path.
Render controlled samples
Generate agreed views and associated scene information.
Agree on camera angles, lighting, background, resolution, and required auxiliary channels. Review sample batches before expanding the generation workload.
Process & organize data
Connect samples with their sources and task requirements.
Keep render parameters and asset mappings alongside imagery. Separate training and evaluation samples around the actual retrieval task.
Train or adapt the model
Run experiments around a defined objective.
Establish an initial retrieval baseline. Adapt the selected model where the data and task call for it, with configurations and experiment records retained.
Compare results
Review candidates, failure cases, and operating requirements.
Review returned candidates for representative references. Inspect confusing shapes, materials, and viewpoints and decide which data or model changes to try next.
Deploy inference
Connect the model and establish an update path.
Expose ranked asset identifiers and source paths through the agreed interface. Define indexing, new-asset processing, and model update procedures.
Illustrative workflow. Exact methods, output fields, and delivery are agreed for each project.
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Tell us what you’re working on.
Tell us about your task, the assets or code you have, and the results your team needs.