Why MagOneAI

MagOneAI achieves true AI sovereignty. Deploy the platform on your infrastructure. Run privately hosted LLMs on your own GPU clusters. Your prompts, data, and model responses never leave your network perimeter.

Any model that supports the OpenAI-compatible API format works out of the box — vLLM, Ollama, LM Studio, TGI, or any custom deployment. Run Llama, Mistral, Qwen, DeepSeek, or any open-source model alongside cloud APIs like OpenAI and Anthropic.

Use cloud models for general tasks. Use private models for sensitive data. MagOneAI lets you do both within the same workflow.

Every workflow runs on Temporal — the same durable execution engine used by Uber, Netflix, Stripe, and Coinbase for mission-critical processes.

What this means for your AI workflows:

• Crash recovery — if a server goes down mid-workflow, it picks up exactly where it left off
• Long-running execution — workflows can run for hours or days without losing state
• Automatic retries — transient failures (API timeouts, rate limits) are handled automatically
• Full observability — every workflow step is tracked, timed, and auditable

No other AI platform offers this level of execution reliability.

Security is not a layer added on top — it's woven into the architecture:

• HashiCorp Vault — all secrets, API keys, and credentials are stored in Vault, not in config files or environment variables
• SSO & MFA — sign in via OpenID Connect (Microsoft Entra ID, Google Workspace, Oracle IDCS) with optional TOTP multi-factor authentication, managed by the platform
• Role-based access control — three scopes (platform Superadmin; organization Owner/Admin/Member; project Admin/Builder/Operator/Viewer) with organization-level isolation
• Full audit trails — every agent execution, every tool call, every data access is logged and attributable
• Encryption — data at rest and in transit, with no exceptions
• Organization isolation — each business unit's data, agents, and workflows are completely separate

Fully autonomous agents sound impressive in demos. In production, they're a compliance nightmare — you can't audit what they'll do, predict their behavior, or explain their actions to regulators.

MagOneAI takes a structured autonomy approach:

• You define the workflow structure — the sequence of steps, decision points, and parallel branches. This is deterministic and auditable.
• AI handles the reasoning — within each step, agents use LLMs, tools, and knowledge bases to generate intelligent outputs.
• Guardrails are built in — each agent has defined tools, permissions, and model access. Nothing operates outside its scope.

The result: workflows that are both intelligent and predictable. Your compliance team can audit the structure. Your agents deliver the intelligence.

MagOneAI uses the Model Context Protocol (MCP) — Anthropic's open standard for connecting AI agents to external tools and services.

Pre-built connectors:

• Google — Calendar, Gmail
• Microsoft — Outlook Calendar, Outlook Email
• Web — Search, scraping
• Data — Text-to-SQL via Vanna

Adding custom tools is straightforward. Any service with an API can become an MCP tool. OAuth flows for third-party services are handled by the platform automatically.

Problem: Sales reps spend 60% of time on research instead of selling.

With MagOneAI: A sales rep says "Prepare me for my meeting with Emaar tomorrow." The workflow triggers parallel agents that search CRM for past interactions, pull latest company news, check social media activity, review competitor movements, and generate a structured meeting prep document — all in under two minutes.

Impact: 2-hour manual research reduced to a 2-minute AI-powered conversation. Reps walk into meetings better prepared, with more time to close deals.

Problem: HR teams answer the same policy questions hundreds of times per month.

With MagOneAI: New employees ask "How do I apply for annual leave?" and the AI agent searches the HR knowledge base (via RAG), finds the relevant policy, provides step-by-step instructions, and links directly to the leave portal. It handles follow-ups like "What about emergency leave?" without human intervention.

Impact: Instant, accurate answers 24/7. HR teams reclaim hundreds of hours per quarter for strategic work.

Problem: L1 support tickets overwhelm the help desk.

With MagOneAI: An employee reports "My VPN isn't connecting." The AI agent checks the known issues database, runs through diagnostic questions, provides troubleshooting steps, and either resolves the issue or creates a pre-triaged ticket routed to the right specialist team — with full context attached.

Impact: 40% of tickets resolved without human intervention. Remaining tickets arrive pre-triaged, reducing resolution time by 60%.

Problem: Compliance teams manually review contracts and regulatory documents — slow, expensive, and error-prone.

With MagOneAI: Upload a contract. An AI agent extracts key terms, flags non-standard clauses, cross-references against compliance policies (via RAG), and generates a structured review summary. A private LLM handles the processing — sensitive contract data never leaves your infrastructure.

Impact: Contract review time reduced from days to minutes. Compliance coverage increases from sampling to 100% review.

MagOneAI supports any LLM provider:

• Cloud APIs — OpenAI (GPT-4, GPT-4o, o1), Anthropic (Claude), Google (Gemini) with native function calling
• Privately hosted models — any model served via an OpenAI-compatible API endpoint. This includes vLLM, Ollama, LM Studio, Text Generation Inference (TGI), and any custom deployment running Llama, Mistral, Qwen, DeepSeek, Phi, or other open-source models

You can use different models for different agents within the same workflow.

If you run a model server that exposes an OpenAI-compatible API endpoint (the /v1/chat/completions format), MagOneAI can connect to it as a provider. You configure the endpoint URL and any required API keys in the Admin Portal, and agents can use it like any other model. No code changes needed.

MagOneAI deploys on your infrastructure. You subscribe to the platform and run it in your own cloud account (AWS, Azure, GCP) or on-premise data center. Docker Compose for development and testing, Kubernetes for production. Your data never leaves your environment.

MagOneAI is a subscription-based platform. Flat pricing — no per-execution fees, no per-agent fees, no per-user surcharges. You manage your own LLM API costs directly with your providers (or eliminate them entirely by running private models). Contact us for pricing details.

Google Calendar, Gmail, Outlook Calendar, Outlook Email, Web Search, and Text-to-SQL (via Vanna). All integrations use the Model Context Protocol (MCP). Adding custom MCP tools for your internal systems is straightforward.

Yes. If you prefer not to manage the infrastructure yourself, we offer a fully managed deployment option. Same platform, same capabilities — we handle the infrastructure, monitoring, and updates.

• First workflow running: Days, not weeks
• Production deployment: 1-2 weeks
• Full enterprise rollout with custom integrations and private LLMs: 2-4 weeks

This compares to 6+ months for building equivalent infrastructure from scratch or implementing enterprise AI vendor platforms.

Yes. Model access is configured at the organization and project level. Your data science team might use GPT-4o for general tasks and a private Llama deployment for sensitive analysis. Your support team might use Claude for customer-facing responses. Each team gets exactly the models they need, governed by admin policy.