Choose the stage your team needs help with, or connect several into one project. The inputs, environment, and deliverables define the work.
6 service areas
Define the intended inputs, outputs, and environment. Prepare evaluation samples, configure training, and adapt a selected foundation model around the task.
Package the trained model for a specified environment or connect it through a callable interface in an internal tool.
Organize images, video, 3D resources, and scene files. Convert formats and preserve their relationships to sources, parameters, and versions.
Set camera, lighting, material, and scene conditions. Generate multi-view images and the metadata required for the task.
Configure dependencies and execution requirements around your models, code, and available compute environment.
Connect preparation, rendering, processing, training, inference, and output management. Establish how new assets and model versions enter the workflow.
For a studio asset library, scope can include camera views, background conditions, source identifiers, material metadata, and an update procedure for new content.
Every task begins with the objective, the materials available, and how the result will be used.
| Project area | Typical inputs | Engineering work | Possible delivery |
|---|---|---|---|
| Model adaptation | Base model, samples, target task, operating environment | Data preparation, fine-tuning, evaluation, inference setup | Model, configurations, experiment notes, deployment materials |
| Rendered datasets | 3D assets, materials, scene conditions, required data fields | Rendering setup, batch generation, mappings, output checks | Dataset, generation settings, processing tools, field documentation |
| Compute workflows | Code, dependencies, source locations, execution requirements | Environment setup, task execution, automation, updates | Environment, scripts, execution records, operating instructions |
| Asset retrieval example | Asset library, identifiers, source paths, representative queries | Multi-view data, retrieval adaptation, indexing, interface deployment | Index, model, callable interface, asset-update tools |
The asset-retrieval row is illustrative. Exact outputs depend on the agreed project scope and source environment.
Our available GPU resources include consumer and data-center hardware with interconnect capability. We plan execution around the memory, communication, graphics, and runtime needs of each task.
Training, rendered-data generation, and batch inference have different execution patterns. The workflow connects them while keeping their requirements explicit.
Discuss your compute needs ↗Tell us about your task, the assets or code you have, and the results your team needs.