Model selection and benchmarking.
Compare candidate models against the actual task. Review output quality, response behavior, resource use and the effect of the chosen model on the workflow.
Services · Private AI and infrastructure
Put deployment requirements at the center of the AI solution.
The choice between a cloud service and a privately hosted model depends on the task, the information involved and the way the system needs to operate. A deployment decision should account for output quality, data flows, external dependencies, response time and the cost of maintaining the service.
What we make.
UnboxedAI evaluates open-model workflows on infrastructure it operates, beginning with a focused use case and representative examples. We define the proposed boundary before the pilot so your team understands where information is processed and what remains dependent on other services.
Private workflow design.
Map the information sources, model calls, storage and review steps. Specify isolation, access and external-service requirements for the agreed environment.
GPU-supported processing.
Evaluate inference, rendering or other processing needs against available capacity. Scheduling, throughput and resource requirements form part of the delivery scope.
Deployment, integration and care.
Connect the evaluated workflow to its intended interface or application. Define updates, monitoring responsibilities, backups and support coverage where included in the agreement.
Model operations and integration.
Connect model services to applications, APIs and data workflows. Define model versioning, evaluation, monitoring and update processes around the intended operating environment.
What you get.
A clear deployment design helps a buyer compare options on operational fit. It can provide greater control over specific processing steps while making the associated limitations and maintenance responsibilities visible.
Typical outputs include a deployment diagram, data and dependency summary, model evaluation, resource estimate and an agreed implementation plan.
On-premises, sovereign or in-country hosting is not implied and requires a separately verified arrangement.
Relevant work.
Current private-model evidence comes from internal studio work.
Our own machines, on record.
Studio Blender render.GPU server
Two NVIDIA L40S GPUs do our rendering and local AI.
CPU power
Two 128-core machines run builds, simulations and video encodes.
Local models
Tested on six kinds of routine studio job, October 2026.
Registry
Hundreds of tested skills and tools, kept in one studio registry.
How we work
AI proof of concept
Test a focused use case using representative examples and agreed measures for quality, usefulness and operating cost.
4-6 weeksSupport and care
Maintain the agreed system or website through defined hosting, updates and ongoing change support.
MonthlyTools and technologies relevant to your project.
AI creates value when the model, workflow and delivery method fit the business need. Our service approach brings together AI-assisted development, language and multimodal models, voice technology, generative production and private deployment options. The technology selection is guided by the task, available data, integration requirements and quality expected from the result.
- Meta Llama
- Qwen
- DeepSeek
- Ollama
- Hugging Face
- NVIDIA
The ecosystem below identifies relevant technologies for project selection and integration. It is not a declaration that every listed platform is currently deployed by UnboxedAI. Final choices are defined within each engagement. Logos identify technologies only and remain the property of their owners. No partnership, certification or endorsement is implied.
See the full list
AI models, data and private deployment
- Meta Llama Open-weight model family
- A candidate for suitably scoped private or self-managed language-model applications, subject to the selected model’s license, hardware and evaluation results.
- Qwen Model family
- A candidate for language, coding and supported multimodal tasks. Deployment and licensing depend on the specific checkpoint and hosting arrangement.
- DeepSeek Model family
- A candidate for reasoning, language and coding workflows. Compare the chosen model’s behavior, operating cost and deployment requirements against the project brief.
- Hugging Face Model and machine-learning ecosystem
- Supports discovery and evaluation of models and related libraries. Relevant to model selection, experimentation and integration; each model has its own terms and requirements.
- Ollama Local model runtime
- Supports running compatible models locally. Relevant to prototypes and selected private workflows; it is one component of a deployment architecture.
- PyTorch Machine-learning framework
- Supports custom model development and inference workflows. Relevant to predictive analytics, computer vision and specialist model work where appropriate data is available.
How we select and integrate AI
We define the task and success criteria, compare suitable models and tools, and test representative inputs before selecting an implementation. The assessment covers output quality, integration needs, data handling, hosting, latency and operating cost. Human review, system testing and clear responsibility remain part of delivery.
Explore capabilities across our services.
Explore integrated capabilities within our six core services, from brand and launch communication to interactive experiences, digital engineering, voice and simulation.
Brand and campaigns
Develop a clear identity, practical brand guidance and launch assets that connect the product to its market.
Games and interactive
Create browser experiences, playable concepts and interactive product demonstrations around a defined audience and purpose.
3D CAD and print
Develop product models, CAD geometry and print-ready outputs with agreed references, dimensions and file requirements.
Voice and dubbing
Support communication through voice-over, transcription, subtitles and dubbing. Language coverage and review are established for the brief.
Simulation and virtual environments
Develop aviation, medical, industrial and scenario-based simulations for training, analysis and decision support. Combine interactive environments, computational models and visualization around the intended users and objectives.
Questions.
Does server ownership guarantee privacy?
No. The relevant controls depend on access, data movement, logs, external services and operating practices as well as the hardware.
Can a cloud service be the better option?
Yes. The decision should follow task quality, data requirements, maintainability and cost rather than a preference for one deployment model.
What should we unbox next?
Tell us what you want to improve, build, test or launch. We will help identify the requirements and a suitable starting point.
Discuss your deployment requirements