“We didn't set out to run AI projects. We set out to build a bank where intelligence is part of the foundation. As an ethical, Shari'ah-compliant bank, we hold ourselves to a high standard on how technology treats our customers and their data. This partnership lets us move fast on AI without ever compromising on that. Everything runs inside our own environment, governed end to end, with a UAE partner who shares our ambition for this country.”
Enterprise Agentic AI Orchestration PlatformYour AI Workforce. Built in Days. Governed Always.Owned by You.
The enterprise agentic AI orchestration platform: build, deploy, orchestrate and monitor agentic AI workflows in production. Deterministic execution, full audit trails, and deployment inside your own infrastructure. One platform, under your control.
The use case library
Agentic AI Use Cases in Production. Not a Roadmap.
A working library of enterprise AI workflows across industries. Every entry runs in production, under governance, on your own infrastructure. You name it, you build it.
MagOneAIMagure Tech Middle East › Use Case Library
13 entries · click anyBanking · the workflows worth building first
KYB & KYC verification
Documents verified in parallel, exceptions to an officer
weeks → daysto onboard a client
Cheque clearance
Read and validated without re-keying
~40% fasterclearing cycle
AML alert closing
Investigated, documented, recommended
~35% lessanalyst effort per alert
Loan document processing
Applications read, checked and packaged
days → hoursto a credit-ready file
Trusted in Production By
80+ enterprise clients · ISO 42001, ISO 27001, ISO 9001 certified · SOC 2 Type II · GDPR compliant.
What is MagOneAI
The Unified Enterprise Agentic AI Orchestration Platform Where Your Teams Do Real Work.
Not another chatbot. MagOneAI turns business processes into governed agentic AI workflows: AI where work needs reasoning, deterministic rules where it needs certainty, human sign-off where decisions carry risk, connected to the systems you already run.
Plays once in view
Completed7 steps · 1 failed and retried · 1 approval · audit log written
Activity timeline7/7 steps1 failedillustrative run
HUMAN TASKReviewer sign-off· waiting for reviewer · approve or request changes…
HUMAN TASKReviewer sign-off· approved4m 12s
ENDEnd· delivered · audit line written0.2s
01First
Build
Describe the process, connect your data, compose it on the canvas from five blocks. AI where you need reasoning, rules where you need certainty, no code.
The ask is typed, and the blocks assemble into a workflow.
02Then
Deploy
It runs in production on the model and infrastructure you chose. First workflow in hours, published as an app, an API or into the Hub.
The run executes: parallel branches, on your model, inside your perimeter.
03Always
Govern
Every step logged, replayable and attributable. Role-based access, model policy and human approval gates where judgment matters.
A human gate holds the run until someone signs; every step lands in the audit line.
New: SuperAgent
One Agent. Many Hands.
SuperAgent multitasks across controlled and creative workflows, orchestrating tools, data and sub-agents in a single conversation, inside the guardrails you set.
The Unified Agentic AI Orchestration Platform
One Control Plane for Every Model, Cloud and System.
Any LLM provider, hot-swappable per agent. Any deployment target: cloud, on-premise, air-gapped. Your ERP, CRM and databases via standard MCP connectors. AI agent orchestration and AI governance in one control plane, with no rip-and-replace, no vendor lock-in.
Any Model Provider
Route across providers. Swap without rewriting a workflow.
- OpenAI
- Anthropic
- Huawei MaaS
- Mistral
- Qwen
- Llama
- DeepSeek
Any OpenAI-compatible endpoint
Any Cloud
Deploy where your regulator lets you. Yours, not ours.
- On-prem / private data centre
- Oracle Cloud (OCI)
- AWS
- Microsoft Azure
- Google Cloud
- Huawei Cloud
Air-gapped and sovereign-ready
Any System
Governed connectors into the systems where work happens.
- SAP
- Salesforce
- Oracle EBS / Fusion
- Microsoft SharePoint
- Snowflake
40+ MCP connectors
MagOneAI: One Control Plane
Orchestration, guardrails, RBAC, cost attribution and audit trails across every model, cloud and system above.
- No model lock-in
- No rip-and-replace
- No rented infrastructure
The AI deployment gap
Why Enterprise AI Stalls in Pilots.
Enterprise AI stalls between pilot and production. The blockers repeat: governance, hallucination risk, cost attribution, integration debt. Each one is answered by an architectural mechanism, not a policy document. Pick the sentence you hear most in your own meetings.
What we hear
“Our AI pilot impressed everyone. It has been a pilot for a year.”
The mechanism
The Pilot Is the Production System
Same canvas, same runtime, governed from day one. There is no rebuild between demo and deployment. Teams get a running workflow in hours and production in weeks.
- No rebuild
- Hours to first run
- Versioned publish
The Library Is Where You Start. You Name It. You Build It.
Every workflow in the library, and anything you can describe, assembles from five blocks: Agent · Tool · Parallel · Condition · Human Task. Five blocks, infinite governed workflows. Engineering, not marketing.
Supplier evaluationPatient intakeFraud reviewLicence renewalsPayroll auditBoard pack preparationGrant processingRegulatory filingTender scoringDispute resolutionCredit memo draftingKYC refresh
Churn early-warningContract renewalsMedia monitoringField report triageYour process hereDebt collection outreachSite inspection reportsOnboarding checksRefund adjudicationIncident post-mortemsVendor risk scoring
The economics of owning your AI
The Compounding Effect of Enterprise AI.
Agents, connectors and guardrails built for workflow one are reused by every workflow after it: compounding AI that gets cheaper to build with every use case. From scratch, each one pays full price.
Directional, based on Wright’s-law learning curves. The full model, your rates, your currency, every assumption editable, is what we send.
Cost to build each additional workflow
Built from scratch: full cost, every timeOn MagOneAI: falls as reuse compounds
cost per use case ↓
workflow number →
AgentOps architecture
Anatomy of Production-Grade Agentic AI.
The model thinks. The workflow guarantees. Probabilistic reasoning inside deterministic, Temporal-backed orchestration: typed outputs, schema validation, replayable state. Six layers. Click any one for the engineering underneath.
Deterministic workflows: LLMs are probabilistic, your operations are not.
Reasoning is left to the model. The sequence, retries, validation and approvals are not. Same case, same path, every time.
Governance · Observability · Audit trails · RBAC · ISO 42001 · wraps every layer
- AI Agents
- your AI workforce
- AI Automations
- your AI operations
- AI Apps & Assistants
- live in Hub, portals, APIs
What the business consumes. Agents handle reasoning tasks, automations run scheduled or triggered processes, and apps expose both through the Hub, your own portal or an API.
- Consumer Hub
- Embedded API
- Portal widgets
- Drag-and-drop canvas
- five blocks, no code
- Cookbook library
- import, run, adapt
- Test & publish
- versioned, one-click rollback
Business teams compose workflows visually; engineers extend them with custom tools. Everything is versioned, testable against sample runs, and publishable or reversible in one click.
- No-code canvas
- Version control
- Sandbox testing
- Durable execution
- Temporal : survives anything
- Multi-agent & parallel
- teams of AI, coordinated
- Guardrails
- typed, validated outputs
- Human-in-the-loop
- approvals where it matters
Temporal-backed durable execution means a workflow survives crashes, restarts and outages without losing state. Branches run in parallel, outputs are schema-validated before the next step, and human gates pause execution until someone signs.
- Temporal runtime
- Schema validation
- Replayable state
- Enterprise RAG
- cited answers : or “I don’t know”
- Knowledge bases
- vector search over your docs
- Live data
- text-to-SQL on your databases
- Memory
- context across conversations
Retrieval is a required step, not an option: answers carry citations back to the source document or row, and an unsupported question returns an explicit non-answer with an escalation path.
- Vector search
- Citation enforcement
- Text-to-SQL
- ERP · CRM · HCM
- via MCP connectors
- Email · Docs · Drives
- Google, Microsoft & more
- Databases & APIs
- 40+ pre-built tools
- Legacy systems
- simple API wrappers
Tools are exposed through the Model Context Protocol, so one connector serves every agent and every workflow. Credentials sit in HashiCorp Vault, never in a prompt.
- MCP tools
- Vault-held secrets
- OAuth flows
- On cloud
- Neo : fully managed SaaS
- On-premise
- Trinity : your cloud, data centre or air-gapped
- OEM / embedded
- white-label the engine inside your product
The same platform runs as managed SaaS, inside your own perimeter, or fully air-gapped with private models on your GPUs. Deployment choice does not change the workflows you built.
- Kubernetes
- Air-gap ready
- Private LLM serving
“Controlled hallucinations: an architecture, not a promise.”
Swap any layer without touching the rest. Change models, add systems or redeploy anywhere, and the workflows you already shipped keep running.
Enterprise AI security, governance & compliance
Built for Regulators, Not Retrofitted for Them.
Sovereign AI infrastructure, inside your network perimeter, on-premise or fully air-gapped. Data, prompts and private LLMs never leave the environment your regulator approved. Every agent action: logged, attributable, replayable for audit.
Runs in Your Perimeter
Your cloud, your data centre, or fully air-gapped. Nothing leaves.
Policy Engine & RBAC
Role-based access control enforced at runtime, not by convention.
Full Audit Trail
AI observability on every run, prompt and decision. Export-ready.
Data Residency
Sovereign AI per jurisdiction, in the region you choose.
Audited and third-party verified, not badges
ISO 42001
ISO 27001
ISO 9001
SOC 2 Type II
GDPR
Dubai AI Seal
Build vs buy vs rent
Rent, Stitch, Build or Own. The Comparison.
| Capability | Cloud AI platformsHyperscaler AI services | Open-sourceFrameworks & automation tools | Enterprise vendorsProprietary AI suites | MagOneAI |
|---|---|---|---|---|
| Deployment | Their cloud | Self-hosted / library | Their cloud | Your infrastructure |
| Private LLM support | Limited | Manual | Limited | Any OpenAI-compatible |
| Durable execution | Basic | No | Yes | Temporal |
| Enterprise security | Yes | DIY | Yes | Vault + OAuth + RBAC |
| Data sovereignty | No / partial | Yes | No | Full |
| Model lock-in | Their models | None | Their ecosystem | None |
| Time to production | Weeks | Months | 6+ months | Days |
Already running automation tools? Keep them. They move data between apps; MagOneAI does the work that needs reasoning, tools and decisions, and can trigger your existing automations as a step in any workflow.
Customer testimonials
Enterprise AI in Production, Not Demos.
Real deployments, not demos. Banks, governments and insurers run MagOneAI agentic AI workflows in production today. Figures measured from one public-facing government deployment, first 113 days live.
- agent executions completed
- 6,336agent executions completed
- of runs completed without failure
- 99.1%of runs completed without failure
- median response time
- 4.5smedian response time
- answers without a cited source
- 0answers without a cited source
Alliance & Delivery Partners
Microsoft Marketplace
Transactable Against Your Azure Commitment
AWS
Deploy Into Your Own Account
Any LLM
GPT Alongside Claude, Gemini and Private LLMs
Frequently asked
The Questions Your Security and Procurement Teams Will Ask.
- Where does our data live?
- On your infrastructure: your cloud, your data center, or fully air-gapped. With private LLMs on your own GPUs, prompts, data and model responses never leave your network perimeter.
- Which LLMs can we use?
- Any OpenAI-compatible model. GPT, Claude and Gemini in the cloud, or private Llama, Mistral, Qwen and DeepSeek on your own hardware via vLLM, Ollama or TGI, mixed per agent within a single workflow.
- How is this governed and audited?
- Governance is the substrate, not a bolt-on: role-based access control, SSO, policy controls, cost attribution and immutable audit trails on every agent action. Backed by ISO 42001, ISO 9001, ISO 27001, SOC 2 Type II and GDPR.
- How does MagOneAI control hallucinations?
- By architecture. Answers are grounded in your documents and data with citations, outputs are typed and validated before use, and when no source exists the agent says “I don't know” and escalates. In live government use that architecture has run 6,336 executions at a 99.1% completion rate. Its failure mode is a visible non-answer, never a confident fabrication.
- How long until our first agent is live?
- First workflow running in hours, production within weeks. The variable is integration: a workflow on one system runs in hours; one that touches core banking, a CRM and a data warehouse takes weeks.
- Do we need ML engineers?
- No. Business teams build on a drag-and-drop canvas from five blocks: Agent, Tool, Parallel, Condition, Human Task. Your engineers stay on what's unique to your business; the AI infrastructure ships with the platform.
Your First Agent, Live in Weeks.
Bring your hardest process. We build it as a governed agentic workflow in front of your team, on your data model, with your approval gates. That is the demo.
- ISO 42001
- ISO 27001
- ISO 9001
- SOC 2 Type II
- GDPR
- Dubai AI Seal
