What will you build?

Fourteen capabilities. Three deployment models. One regional platform designed for production.

Filter by capability14 use cases
01◎

Conversational AI

Intelligent conversations at production scale

Deploy multilingual assistants, customer support agents and embedded copilots with streaming responses, multi-turn memory and tool calling.

Support automationSaaS copilotsIVR replacement
02⌘

Code generation and assistance

Ship an AI copilot in your IDE or platform

Power completion, generation, refactoring and debugging through familiar APIs and modern open-weight coding models.

Developer toolsInternal toolingCI/CD
03↗︎

Agentic workflows

Agents that reason, plan and execute

Build workflows that call tools, make decisions and complete structured work across business systems.

Task automationTool callingMulti-agent
04⌕

Search and RAG

Ground AI in the knowledge your business trusts

Combine retrieval, embeddings and generation for enterprise search, document Q&A and knowledge assistants.

Knowledge basesSemantic searchPrivate data
05◇

Reasoning and complex problem solving

Structured reasoning for high-value decisions

Use reasoning-optimized models for analytical, legal, financial and multi-constraint planning workloads.

FintechLegaltechPlanning
06◫

Image generation and editing

Production visuals through a simple API

Run image generation, inpainting and brand-specific fine-tuning on optimized regional infrastructure.

Creative toolsE-commerceAdvertising
07◉

Vision and multimodal

Understand images, documents and screens

Combine text and visual inputs to extract data, parse diagrams and build visual question-answering products.

Document processingVisual Q&AData extraction
08≋

Speech-to-text and audio

Transcribe and understand audio in real time

Build multilingual transcription, meeting intelligence, call-center analytics and voice interfaces.

MeetingsCall centersVoice apps
09{ }

Structured outputs and data extraction

Define a schema. Receive reliable data.

Extract entities, classify documents and normalize unstructured inputs into clean typed JSON.

FormsData pipelinesDocument intake
10△

Fine-tuning

Make an open-weight model specific to your business

Train with proprietary data and deploy to a dedicated endpoint without managing GPU infrastructure.

LoRA / QLoRADomain modelsML teams
11⇄

Batch and async processing

Process large workloads on your schedule

Queue high-volume jobs for annotation, content generation, offline scoring and pre-computation.

Bulk generationEvaluationData teams
12⌁

Evaluations and benchmarking

Measure quality, latency and cost before launch

Compare models, score outputs and detect regressions before new versions reach production.

A/B testingQA teamsModel lifecycle
13>_

Sandboxed code execution

Run generated code in an isolated environment

Add safe Python execution to agents, interpreters and analytical applications without exposing your infrastructure.

Agent buildersNL-to-codeAnalysis
14⬡

Enterprise-grade deployment

Keep control of your models, data and compliance posture

Use dedicated environments, zero-retention options, RBAC and regional hosting for sensitive workloads.

GovernmentFinanceHealthcare

Match the operating model to the sensitivity and scale of the workload.

Use shared managed APIs for speed, dedicated endpoints for isolation or full GPU clusters for infrastructure-level control.

01

Managed API

The fastest path from first prototype to production using familiar OpenAI-compatible endpoints.

  • Shared optimized infrastructure
  • Task-based pricing
  • Automatic scaling
02
Most flexible

Dedicated endpoint

A private model environment with managed operations and single-tenant isolation.

  • Custom model choice
  • Zero-retention options
  • Reserved performance
03

GPU cluster

Dedicated GPU capacity with root access for custom runtimes and advanced ML teams.

  • NVIDIA H100 infrastructure
  • InfiniBand interconnect
  • Full runtime control

Deploy your first endpoint with the tools you already use.

OpenAI-compatible APIs reduce migration effort across SDKs, frameworks and existing products.

POST /v1/chat/completions
client = OpenAI(
  base_url="https://api.hyperfusion.io/v1",
  api_key=HYPERFUSION_API_KEY
)

Describe the product. Get the architecture.

Hyperfusion maps your task to the model, compute and deployment mode required for production.

Scope a projectTalk to an expert