The enterprise AI dilemma
Organizations building with AI today face three paths — none of them ideal:Cloud AI platforms
AWS Bedrock, Azure AI, Vertex AICapable, but you pay per execution forever. Your data flows through third-party infrastructure. Switching providers means rebuilding from scratch. You’re renting — not building.
Open-source frameworks
LangChain, CrewAI, n8nGreat for prototypes and hackathons. But going to production means building your own security layer, workflow orchestration, secret management, audit logging, and OAuth flows. That’s 6+ months of engineering before your first real workflow ships.
Enterprise AI vendors
Palantir AIP, C3.aiFull-featured, but $1M+ budgets, 6-month implementations, and consultant-heavy engagements. You get a powerful platform you can barely customize. And you’re still locked to their ecosystem.
The MagOneAI approach
MagOneAI is a subscription-based enterprise platform that deploys on your infrastructure. You get the capabilities of cloud AI platforms, the flexibility of open-source, and the governance of enterprise vendors — without the trade-offs.How it works: Subscribe to MagOneAI. Deploy it on your cloud or data center. Connect any LLM — cloud APIs or privately hosted models. Your data, your models, your infrastructure. We provide the platform, you control everything else.
How we compare
| Capability | AWS Bedrock | Azure AI | n8n | LangChain | Palantir | MagOneAI |
|---|---|---|---|---|---|---|
| Deployment | Their cloud | Their cloud | Self-hosted | Library | Their cloud | Your infrastructure |
| AI-native | Yes | Yes | Add-on | Yes | Yes | Yes |
| Private LLM support | Limited | Azure only | Manual | Manual | Limited | Any OpenAI-compatible |
| Durable execution | Basic | Basic | No | No | Yes | Temporal |
| Enterprise security | Yes | Yes | DIY | DIY | Yes | Vault + OAuth + RBAC |
| Data sovereignty | No | Partial | Yes | Yes | No | Full |
| Model lock-in | AWS models | Azure models | None | None | Palantir | None |
| Time to production | Weeks | Weeks | Months | Months | 6+ months | Days |
What sets MagOneAI apart
AI sovereignty and private model support
AI sovereignty and private model support
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.
Temporal-powered durable workflows
Temporal-powered durable workflows
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
Enterprise security by design
Enterprise security by design
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
- OAuth 2.0 — users authenticate against Google and Microsoft with standard OAuth flows, managed by the platform
- Role-based access control — four-tier hierarchy (SuperAdmin, Admin, Member, 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
Structured autonomy
Structured autonomy
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.
MCP tool integration (Anthropic standard)
MCP tool integration (Anthropic standard)
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
MagOneAI vs n8n
n8n = Automation with AI add-on
“When trigger X happens, do A then B then C.”Every step is deterministic. The LLM is just another node. Great for app-to-app automation — moving data between Salesforce, Slack, and Google Sheets. But not built for AI-native workflows where agents need to reason, use tools, and make decisions.MagOneAI = AI-native workflow platform
Built for intelligent agents from the ground up.Native RAG with vector search. Agent personas with role-specific behavior. Multi-agent orchestration with parallel execution. Durable workflows that survive infrastructure failures. Private model support for data sovereignty.Enterprise benefits
Complete data sovereignty
Deploy on your infrastructure. Run private LLMs. Process sensitive data without it ever leaving your network. Meet GDPR, HIPAA, SOC 2, and data residency requirements out of the box.
Zero model lock-in
Use OpenAI today, switch to a private Llama deployment tomorrow. Any model with an OpenAI-compatible API works. Your workflows don’t change when your model strategy does.
Predictable economics
Flat subscription pricing for the platform. No per-execution fees, no per-agent fees, no per-user surcharges. Scale your AI usage without scaling your AI bill. You manage your own LLM costs directly.
Days to production
First workflow running in days, not months. No infrastructure to build, no security layer to design, no OAuth flows to implement. Subscribe, deploy, and start building.
Use cases
Sales intelligence assistant
Sales intelligence assistant
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.
HR policy and onboarding assistant
HR policy and onboarding assistant
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.
IT support triage
IT support triage
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%.
Document analysis and compliance review
Document analysis and compliance review
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.
Frequently asked questions
What LLMs does MagOneAI support?
What LLMs does MagOneAI support?
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
How does private LLM support work?
How does private LLM support work?
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.What's the deployment model?
What's the deployment model?
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.
What's the pricing model?
What's the pricing model?
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.
What integrations come built-in?
What integrations come built-in?
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.
Do you offer managed hosting?
Do you offer managed hosting?
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.
How long does implementation take?
How long does implementation take?
- First workflow running: Days, not weeks
- Production deployment: 1-2 weeks
- Full enterprise rollout with custom integrations and private LLMs: 2-4 weeks
Can different teams use different models?
Can different teams use different models?
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.