AI Fabrix enables enterprises to scale AI safely inside their own environment with governance, explainability, permissions, and operational control built in.
Manual prompts and rigid workflows break operational control, creating governance gaps, inconsistent decisions, and enterprise risk.
AI Fabrix adds a dynamic trust layer between enterprise systems, data, and AI models.
Most AI systems understand APIs, payloads, and documents. AI Fabrix understands enterprise permissions, operational relationships, governance structures, business ownership, and dynamic business context.
AI Fabrix creates normalized, AI-readable business context across enterprise systems — allowing AI access to follow real business relationships instead of static roles.
Customer-level access automatically aligned to the correct business relationship.
Regional governance context aligned to operational and regulatory boundaries.
Project-aware access that follows real organizational ownership and workflows.
Department structures synchronized to real operational hierarchy and responsibility.
Governance follows projects, customers, departments, and operational ownership instead of static role assignments.
Permissions and policies continuously update from connected enterprise systems as business operations evolve.
Connected enterprise systems are not automatically AI-safe. AI Fabrix continuously validates governance integrity, permission consistency, ownership relationships, and operational trust before AI access is allowed.
AI must remain governed, explainable, permission-aware, auditable, and accountable to real business oversight.
Launch AI in weeks, not years. Start with a single team, prove value quickly, and expand across workflows and systems without rebuilding your architecture.
Launch AI in a controlled operational environment.
Embed governance into existing workflows and data flows.
Scale securely across enterprise systems and policies.
As AI models become commodities, competitive advantage shifts to operational trust, governed access, explainable behavior, and trusted enterprise context.
AI Fabrix does not sell AI applications.
It enables enterprises to build and trust their own AI solutions.
AI Fabrix transforms enterprise systems into governed AI Services that any AI platform can use immediately.
We’ve gathered the most frequent questions about AI Fabrix — from setup to security. Explore the Q&A or start a free trial now.
AI Fabrix is an Azure-native, in-tenant enterprise AI platform that enables secure, governed AI adoption at scale.
It turns fragmented enterprise systems into permission-aware, AI-ready data using open standards—so AI agents, workflows, and applications can run safely in production, not just pilots.
AI Fabrix runs entirely inside your Azure tenant and uses standard Azure services for compute, storage, networking, and identity.
There is no shared SaaS component and no external service accounts. Infrastructure sizing depends on workload and scale, and follows a predictable, infrastructure-based model.
Yes. Security and governance are foundational to AI Fabrix. All data and AI operations run inside your Azure tenant, using Entra ID for identity, ABAC/RBAC for access control, and full audit logging for every data access and AI action. This enables compliance with enterprise and regulated-industry requirements.
Most customers start with a guided deployment inside their Azure tenant. This includes setting up governance with Miso, connecting core systems through CIP, and deploying an initial AI agent or use case. From there, teams expand incrementally using the same governed foundation.
Yes. AI Fabrix integrates with enterprise systems using OpenAPI and Composable Integration Pipelines (CIP). This approach avoids custom SDKs and service accounts, automatically enforcing identity-based access (ABAC/RBAC) and providing full metadata, lineage, and auditability across systems such as CRM, ERP, HR, Finance, SharePoint, and custom APIs.
AI Fabrix offers documentation, reference architectures, and enterprise support options.
Customers also receive guidance on governance setup, integration patterns, and production rollout to ensure AI moves safely from pilot to scale.
