MagOneAI is the enterprise agentic AI workflow platform built for AI sovereignty. Deploy on your infrastructure, connect any LLM, and run production-grade AI workflows with enterprise security from day one.
MagOneAI is an enterprise agentic AI workflow platform that deploys on your infrastructure and gives you complete control over your AI stack — the models you use, the data you process, and the workflows you run.Subscribe to the platform. Deploy it privately. Connect any LLM — from OpenAI and Anthropic to privately hosted open-source models. Your data never leaves your environment.
AI sovereignty
Deploy on your infrastructure. Run private LLMs. No data leaves your environment. Meet the strictest compliance and data residency requirements.
Any model, any provider
OpenAI, Anthropic, or any privately deployed model that supports the OpenAI-compatible API format. Switch models without changing a single workflow.
Production-grade from day one
Temporal for durable execution. HashiCorp Vault for secrets. OAuth 2.0 for integrations. RBAC and audit trails built in — not bolted on.
Build complex, multi-agent AI workflows visually — with parallel execution, conditional logic, human-in-the-loop approvals, and private model support.
KYB document verification
RFP proposal analysis
A Know Your Business (KYB) workflow that processes Emirates ID, trade licenses, and passports in parallel using a privately hosted Qwen3 vision model — sensitive document data never leaves your infrastructure.
KYB verification workflow with parallel AI agents, conditional routing, human approval, and email notification — powered by a private Qwen3-VL model.
An RFP analysis workflow that runs five specialist agents in parallel — Technical, Vendor, Commercial, Compliance & Risk, and Timeline & Delivery — then combines their analyses into a structured evaluation.
RFP proposal analyser with five parallel specialist agents producing a comprehensive procurement evaluation.
What you see in the canvas:
Drag-and-drop nodes — Agent, Tool, Parallel, Condition, and Human Task
Parallel execution — run multiple AI agents simultaneously and combine their outputs
Conditional logic — route workflows based on agent outputs or business rules
Human-in-the-loop — require human approval before critical actions like sending emails
Any model per workflow — use a private vision model for document analysis, a cloud model for text generation
JSON + visual — every workflow is both a visual canvas and a portable JSON definition
MagOneAI is a layered enterprise platform — from the consumption interfaces your teams use, through the AI and workflow engine, down to your enterprise data sources.
Consumption layer
Web interface, mobile apps, API/SDK integrations, and dashboard embeds. Your teams interact with AI through the channel that fits their workflow.
Authentication and security layer
SSO (OIDC) and OAuth for enterprise identity. RBAC and permissions at every level. API key management for programmatic access. Data encryption at rest and in transit.
Conversational and agentic layer
Dialogue management for natural conversations. Multi-agent orchestration for complex tasks. Human-in-the-loop for approval workflows. This is where your AI agents reason, plan, and execute.
MCP tool router
Standard tools (Calendar, Email, Search), database and SQL tools, custom integrations, and external APIs — all connected through the Model Context Protocol. Agents access tools with managed OAuth and credential handling.
Workflow orchestration
Design Studio for visual workflow building. Custom RAG pipelines for knowledge-intensive tasks. Complex multi-model orchestration across agents. All running on Temporal for durable, crash-resistant execution.
Model selection and orchestration
LLMs (cloud and private), machine learning models, and specialized models — all configurable per organization, per project, per agent. Route to the right model for the right task.
AI monitoring and compliance
Action logging, model usage tracking, cost tracking, policy administration, usage statistics, and performance analysis. Full visibility into every AI action across your organization.
Enterprise data sources
Connect to data warehouses and lakes, documents and files, knowledge bases, and external APIs and web services. Your agents work with your data, wherever it lives.
Most enterprise AI platforms force a choice: use cloud-hosted AI APIs and accept data leaving your perimeter, or spend months building infrastructure from scratch. MagOneAI eliminates that trade-off.
Private deployment
MagOneAI runs entirely within your infrastructure — AWS, Azure, GCP, or your own data center. Docker Compose for development, Kubernetes for production. Nothing phones home.
Private LLMs
Run open-source models like Llama, Mistral, Qwen, or DeepSeek on your own GPU infrastructure. Any model that exposes an OpenAI-compatible API endpoint works out of the box — vLLM, Ollama, LM Studio, TGI, or any custom deployment.
True AI sovereignty means your prompts, your data, and your model responses never cross your network boundary. The KYB workflow above demonstrates this — sensitive identity documents are processed by a privately hosted Qwen3 vision model, entirely within the enterprise perimeter.
MagOneAI is model-agnostic. You configure which LLM providers and models are available at the organization level, and agents use them through a unified interface.
Provider type
Examples
How it connects
Cloud AI APIs
OpenAI GPT-4o, Anthropic Claude, Google Gemini
Direct API integration with native function calling
Privately hosted models
Llama, Mistral, Qwen, DeepSeek, Phi
Any endpoint supporting OpenAI-compatible API format
Managed private AI
Azure OpenAI (OpenAI-compatible)
Same OpenAI-compatible integration
You can use different models for different agents within the same workflow. A fast, small model for classification. A large reasoning model for analysis. A private vision model for document processing. MagOneAI handles the routing.
MagOneAI is organized into three portals, each serving a different role in your organization:
Admin Portal
For IT and platform teamsManage organizations, users, LLM provider configurations, security policies, and resource quotas. Govern who can use what, across the entire platform.
MagOneAI Studio
For business teams and developersCreate AI agents with personas and tools. Build multi-step workflows with a visual canvas. Connect to external systems via MCP. Test and deploy — without writing infrastructure code.
MagOneAI Hub
For end usersChat with AI assistants. Run workflows. Track execution progress in real time. Review conversation history. No training required — just type.
MagOneAI follows a structured autonomy architecture: you define the workflow structure for predictability and compliance, AI handles the reasoning and intelligence at each step.
1
Define your workflow
Use the visual workflow builder to define the structure — triggers, parallel branches, agent steps, conditional logic, and human approval gates. The structure is deterministic and auditable.
2
Configure AI agents
Configure agents with personas, tools, and LLM settings. Assign specific models and permissions to each agent.
3
Connect your tools
Use MCP (Model Context Protocol) to connect agents to Google Calendar, Gmail, Outlook, Web Search, databases, CRMs, or any custom API. OAuth flows are handled automatically.
4
Deploy to production
Every workflow runs on Temporal — the same durable execution engine used by Uber, Netflix, and Stripe. Workflows survive server crashes, network failures, and can run for hours or days without losing state.
