Platform Overview

The NexusAI Platform is an enterprise-grade infrastructure designed to accelerate the development, deployment, and scaling of artificial intelligence systems. It provides a unified interface for managing models, orchestrating AI agents, processing real-time data streams, and ensuring compliance across multi-cloud environments.

ℹ️ Note

This documentation covers the NexusAI Cloud API. For self-hosted or on-premise deployments, refer to the Infrastructure Guide.

Core Components

The platform is built around four modular pillars that work seamlessly together:

Model Hub — A curated repository of pre-trained foundation models, fine-tuned variants, and community-contributed architectures.
Agent Framework — A stateless orchestration layer that enables autonomous decision-making, tool use, and multi-step reasoning.
Inference Engine — Low-latency, auto-scaling compute optimized for streaming, batch, and edge deployments.
Analytics & Observability — Real-time metrics, drift detection, cost tracking, and audit logging.

Authentication & Authorization

All API requests require authentication via API keys or OAuth 2.0 client credentials. Keys are scoped to specific projects and roles.

cURL

curl -X POST https://api.nexusai.com/v3/models/infer \
  -H "Authorization: Bearer $NEXUS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nexus-v3-ultra",
    "input": "Analyze sentiment for: The deployment was seamless.",
    "max_tokens": 256
  }'

⚠️ Security Notice

Never expose your API keys in client-side code or public repositories. Use environment variables and our SDK's built-in secret management.

Environment Configuration

Configure the platform using a .env file or infrastructure-as-code tools. Supported variables:

Variable	Type	Description	Default
`NEXUS_API_KEY`	String	Primary authentication credential	—
`NEXUS_REGION`	String	Deployment region (us-east, eu-west, ap-southeast)	`us-east`
`NEXUS_TIMEOUT`	Integer	Request timeout in milliseconds	`30000`
`NEXUS_RETRY_POLICY`	String	Exponential backoff configuration	`default`

Quick Start: Python SDK

Install the official SDK and initialize a client in under 60 seconds:

Python

# Install: pip install nexusai
from nexusai import Client

# Initialize with API key from environment
client = Client(api_key="$NEXUS_API_KEY")

# Run inference with streaming
for chunk in client.chat.stream(
    model="nexus-v3-ultra",
    messages=[{"role": "user", "content": "Summarize this report."}],
    temperature=0.7
):
    print(chunk.delta, end="")

System Architecture

NexusAI follows an event-driven, microservices architecture. Requests are routed through a global edge network, validated against your project's rate limits, and dispatched to optimized GPU/TPU clusters based on model requirements.

💡 Pro Tip

Use the client.models.list() endpoint to discover available regions and latency benchmarks for your use case. Lower-latency regions are ideal for real-time agent workflows.

Deployment Models

Serverless — Auto-scales to zero, pay-per-token. Best for variable workloads.
Dedicated Clusters — Reserved compute with guaranteed throughput. Ideal for high-frequency production pipelines.
Edge Runtime — Lightweight containerized models for on-device or air-gapped environments.

Next Steps

Continue exploring the platform documentation:

API Reference — Complete endpoint documentation with request/response schemas
Agent Framework — Build autonomous systems with tool use and memory
Fine-Tuning Guide — Custom model training on your proprietary datasets