AIRL — AI Research Lab
Applied generative-AI R&D · HASAI 3D
Build the AI workflow your production actually needs
From experimental AI models to repeatable production workflows.
AIRL is HASAI 3D’s applied generative-AI research and deployment capability for enterprise clients. We design, test, benchmark, and operationalize custom AI workflows on local GPU infrastructure, then integrate proven workflows into real production pipelines.
We combine ComfyUI workflow engineering, model evaluation, local inference, and 3D-pipeline integration to help production teams turn fast-moving AI technology into a dependable capability.
Model-agnostic. Built around your workflow. We select models and tools around your requirements, data, software stack, security constraints, and target outputs. The goal is a repeatable production system tailored to your team.
What we can build with you
ComfyUI workflow R&D
Design, test, and optimize node-based pipelines for image and video generation, editing, refinement, upscaling, inpainting, conditioning, and multi-stage generation.
Model & LoRA evaluation
Benchmark base models, fine-tunes, LoRAs, quantizations, and supporting components against your quality, speed, memory, and reproducibility criteria.
Local & on-prem inference
Deploy workflows on client-controlled or HASAI-controlled GPU workstations where privacy, data control, latency, or model flexibility matter.
3D-to-AI pipelines
Connect Unreal Engine and other digital content creation tools to generative workflows using beauty renders, depth, normals, masks, poses, and metadata.
AI post-production
Use generated or rendered images as inputs for realism enhancement, relighting, detail passes, image-to-image transformation, inpainting, upscaling, and video generation.
Workflow automation & APIs
Expose proven pipelines through local APIs, scripts, editor tools, or orchestration layers so operators can use them without manually building or running ComfyUI graphs.
Hardware & performance optimization
Benchmark VRAM, RAM, inference speed, quantization, offloading, and multi-GPU strategies against the workload you need to run.
Private model experimentation
Evaluate models and workflows that are unsuitable for restrictive hosted services but are lawful and appropriate for your business requirements.
Questions AIRL helps you answer
We reduce the uncertainty and engineering work between a promising model and a production workflow your team can use repeatedly.
- Model selection: Which model and supporting tools work best for our use case?
- Deployment: Can the workflow run locally or on our existing hardware?
- Control and consistency: How can we preserve identity, pose, geometry, or composition across passes?
- 3D integration: How can deterministic 3D data guide generative models?
- Automation: How can generation, refinement, inpainting, upscaling, and video work together without manual handoffs?
- Team adoption: How can artists and operators use the workflow without becoming AI specialists?
- Data control: How can sensitive assets and prompts stay inside our environment?
From research to production
1. Discovery & target definition
Define the production problem, quality bar, constraints, and measurable success criteria.
2. Research sprint
Test candidate models, nodes, LoRAs, and workflow architectures against those requirements.
3. Prototype workflow
Produce a working ComfyUI or hybrid workflow with documented inputs and outputs.
4. Benchmark & harden
Measure quality, consistency, speed, VRAM and RAM use, failure modes, and reproducibility.
5. Integrate
Connect the workflow to Unreal Engine, other content creation tools, asset storage, local APIs, or other client systems where required.
6. Operationalize
Package the workflow for repeatable use with documentation, handoff, maintenance, and optional support or a service-level agreement.
What we measure
Evaluation follows the production requirements defined with your team. Depending on the engagement, it can include:
| Evaluation area | What we examine |
|---|---|
| Model and workflow fit | Candidate models, LoRAs, and workflow configurations tested against the target use case |
| Quality and consistency | Output quality, identity and composition control, and changes across iterations |
| Performance | Inference time, VRAM and RAM use, and hardware trade-offs |
| Private deployment | How the workflow operates in a local or client-controlled environment |
| 3D integration | The path from Unreal Engine or DCC output through AI processing to final output |
| Operational use | Reproducibility, failure modes, and manual steps replaced by automation |
Ways to work with AIRL
- AI research and feasibility sprints — fixed-scope exploration of a production problem and candidate approaches.
- Custom workflow development — develop and refine the pipeline your team needs.
- Integration and deployment — connect proven workflows to your tools and infrastructure.
- Maintenance and optimization retainers — ongoing workflow support and improvement.
- Private AI infrastructure consulting — evaluate local and on-prem deployment options for your workloads.
- HASAI 3D enterprise deployments — include AIRL as the generative-AI experimentation and validation layer within a broader implementation.
Private creative production
AIRL can support lawful adult-content and NSFW generative workflows for businesses that require them, including model evaluation, workflow design, realism and refinement pipelines, and local deployment.
Engagements are limited to lawful, consensual, authorized content and appropriate client data. This is one of AIRL’s private deployment capabilities, alongside its broader enterprise production work.
Potential applications include adult entertainment, virtual characters, synthetic influencers, game and VFX production, and private creative tooling with legitimate requirements beyond mainstream hosted-platform policies.
Part of the HASAI 3D production pipeline
AIRL connects deterministic 3D production with generative AI. Unreal Engine and other content creation tools provide structured inputs; research and benchmarking identify useful models and techniques; production validation turns those experiments into repeatable workflows.
Findings that prove useful across engagements feed into reusable HASAI knowledge and product components. Our approach is to research broadly, productize what repeats, and integrate what creates customer value.
Bring us your production challenge
Tell us what you want to produce, which tools and hardware you use, and the quality, privacy, or performance constraints the workflow must meet. We can help define a focused research, development, or deployment engagement.