01
Describe
Start with the result the product needs.
State the task, data type, expected volume, languages and success criteria. No instance selection is required.
›
Describe an AI task…
Describe the workload. Get the architecture and price.
Define an AI task in plain language. Hyperfusion maps it to the right model, deployment mode, infrastructure and operating approach.
Project architectSTEP 1 / 3
I need to process multilingual contracts and return structured risk findings.
Matching task to model and infrastructure…
ModelQwen3-32BModeDedicated endpoint
Move from a business requirement to a production system.
The interface guides non-specialists while preserving the controls engineering teams need.
02
Configure
Hyperfusion maps the technical system.
The task is matched to an open-weight model, latency profile, deployment mode and regional controls.
- Model and context requirements
- Managed, dedicated or cluster deployment
- UAE region and data controls
- Evaluation and production SLA
MODELQwen3-32B
+REGIONUAE
+MODEDedicated
03
Deploy
Receive a production-ready endpoint or environment.
Provision through the console, use a familiar API and scale with managed operations or dedicated capacity.
Endpoint onlineAPI key generatedMonitoring active
POST /v1/chat/completions
200 OK · 42 msScope a representative workload.
Select a prompt or write a requirement. Watch a requirement map to a model, deployment mode and commercial scope in seconds.
Hyperfusion Architect Ready
Recommended modelQwen3-32B
DeploymentDedicated endpoint
RegionUAE / DXB
Commercial modelPredictable monthly task scope
The operating layer stays with the workload.
Production AI requires capacity management, monitoring, integrations and ongoing optimization—not only a model endpoint.
GPU operations
Capacity, cluster health, deployment and continuous optimization managed by infrastructure specialists.
IT integration
Connect identity, networking, data and application systems through a tailored enterprise architecture.
AI consultancy
Translate use cases into model strategy, evaluation plans, implementation and production governance.
Model lifecycle
Evaluate versions, fine-tune domain models and track quality, latency and cost as requirements evolve.
Keep the application layer familiar.
Change the endpoint, choose the model and keep building with common SDKs and frameworks.
client = OpenAI(
base_url="https://api.hyperfusion.io/v1",
api_key=HYPERFUSION_API_KEY
)Describe the outcome. Leave with an architecture and a price.
Bring the task, the expected volume and the data requirements. Hyperfusion maps the model, deployment mode and commercial approach.