Features

Explore the core capabilities of AI Fabrix — built for secure, enterprise-scale AI inside your Azure tenant.

Getting Started
  • Deploy from Azure Marketplace – Provision baseline stack in your Azure tenant.
  • Integrate Data Sources – Connect SharePoint, Teams, CRM, ERP, HR, Finance.
  • Configure Identity & Governance – Enable Entra ID SSO, RBAC, audit controls.
  • Build Workflows & Applications – Use Flowise and OpenWebUI to create use cases.
  • Scale Securely – Move from Dev → Test → Prod environments with predictable ROI.
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    Platform Overview

    AI Fabrix is an enterprise AI control and execution fabric that runs entirely inside your Azure tenant. It extends Microsoft’s cloud and AI services with the governance, identity, and environment control required to move AI from pilots to production — safely and predictably.

    Unlike external SaaS AI platforms, AI Fabrix does not copy data, bypass identity systems, or introduce hidden control planes. All AI agents, workflows, and integrations operate using your existing Azure infrastructure, Entra ID identities, and security boundaries.

    At the center of the platform is Miso Controller — the enterprise control plane that governs identity, access, environments, policies, and audit across all AI workloads.

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    Miso Controller – Enterprise AI Control Plane

    Miso Controller is the governance layer of AI Fabrix. It separates control from execution, ensuring that AI runtimes remain lightweight while all enterprise rules are enforced consistently.

    Miso centrally manages:

    • Identity and group-based access
    • Environment lifecycle (Dev → Test → Prod)
    • Application roles and permissions
    • Policy evaluation and audit logging

      This allows AI to operate under the same identity, permission, and compliance rules as human users — without duplicating RBAC logic across tools.

      Environment-Aware Authorization

      AI Fabrix introduces an environment-first authorization model designed for enterprise delivery.

      Applications define their roles and permissions once. Miso binds those roles to enterprise identity groups per environment, allowing different access levels in Development, Test, and Production.

      This enables:

      • Full experimentation in Dev
      • Controlled validation in Test
      • Least-privilege execution in Production

      —all without rewriting access logic or maintaining separate identity models per tool.

      Identity-First, Standards-Based by Design

      AI Fabrix integrates with enterprise identity providers using open standards:

      • OpenAPI (via CIP) – Standardized System Integration
      • OIDC (OpenID Connect) – Enterprise Authentication
      • SCIM – Automated Identity & Group Provisioning
      • MCP (Model Context Protocol) – Governed AI Actions
      Governed AI Agents & Actions

      AI Fabrix enables AI to perform real enterprise actions — safely.

      AI agents cannot directly modify enterprise systems. All write actions are routed through Miso’s governed execution layer, where permissions are evaluated and every action is logged.

      This ensures:

      • Approved actions only
      • Full auditability
      • No uncontrolled automation

      AI doesn’t bypass governance — it enforces it.

      Metadata-Driven AI & Permission-Aware RAG

      AI Fabrix enables attribute-based access control (ABAC) using business metadata.Labels derived from CRM, collaboration, and HR systems are used to enforce permission-aware retrieval across documents and vectors. AI responses are generated using only the data the user is allowed to see — in context.

      This enables safe, explainable Retrieval-Augmented Generation at enterprise scale.

      Production Readiness

      AI Fabrix is designed for production deployment, not experimentation.

      • Environment lifecycle management
      • Controlled promotion of agents and workflows
      • Consistent identity and policy enforcement
      • Predictable scaling and infrastructure-based costs

      What works in pilot works in production — without redesign.

      Compliance & Trust

      Every AI action is logged, auditable, and traceable.

      AI Fabrix aligns with ISO-27001 principles and supports regulated industries by providing centralized audit logs, policy decision tracking, and environment-level observability — all inside the customer tenant.

      Open Standards. No Lock-In. Full Control.

      AI Fabrix is built on open, proven standards such as OpenAPI, OIDC, SCIM, and MCP. Customers retain full ownership of their infrastructure, data, and exit strategy — always.

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      Governed System Access (CIP Runtime)

      AI Fabrix provides a governed access layer for enterprise systems. All integrations run inside the customer’s Azure tenant and operate under explicit identity, policy, and metadata controls — ensuring AI agents can only access data and actions they are entitled to perform.

      Delegated API Access (OpenAPI-based)

      CIP uses OpenAPI-driven delegation to allow AI agents and applications to call enterprise APIs safely and predictably.

      • REST/HTTP (JSON), GraphQL, SOAP (legacy)
      • OAuth2, mTLS, API keys stored in Azure Key Vault
      • Rate limits, retries, circuit breakers enforced per environment

      Capabilities

      • Request signing and verification enforced by Miso policies
      • OpenAPI schema validation to prevent malformed or unsafe calls
      • Webhook verification with replay protection
      • Fine-grained egress controls per connector and environment
      Federated Data Access

      CIP enables read-only, least-privilege access to enterprise databases where appropriate. AI Fabrix avoids data duplication and operates directly on source systems unless explicitly configured for indexing or semantic enrichment.

      • Federated read via service accounts with least privilege.
      • Batch extract and CDC (change data capture) where sources support it.
      • Caching via Redis; vectorization into PostgreSQL/pgvector when required.

      Common engines

      • Azure SQL / SQL Server, PostgreSQL, MySQL, Oracle.
      • Data warehouses (read patterns): Azure Synapse, Snowflake, BigQuery (via APIs).
        Question
        Without CIP
        With CIP
        Is AI controllable?
        Without CIPSometimes
        With CIPYes
        Is risk manageable?
        Without CIPUnclear
        With CIPExplicit
        Is scaling safe?
        Without CIPNo
        With CIPYes
        Is cost predictable?
        Without CIPNo
        With CIPYes
        Is AI defensible to regulators?
        Without CIPDifficult
        With CIPStraightforward

        Controls

        • Connection pooling, read-only roles, masked views for PII/PHI.
        • Batch extract and CDC (change data capture) where sources support it.
        • Caching via Redis; vectorization into PostgreSQL/pgvector when required.
        File System Integration

        Sources

        • SharePoint, OneDrive, Teams Files, Azure Blob/Files; SFTP for legacy file drops.

          Document processing

          • Incremental crawls based on modified/created timestamps.
          • File-type filters (.docx, .pptx, .pdf, .txt, .xlsx, .csv, etc.).
          • Inherited folder metadata (e.g., Deal Name, HubSpot ID) merged into file records.
          • OCR where needed; text/metadata extraction pipelines with retry and dead-letter queues.

          Security

          • Source-permission checks on retrieval; metadata tags drive access control in RAG.
          Cloud Service Integration

          Azure-first

          • Entra ID (SSO, SCIM), Key Vault, Storage, App Service/Container Apps, Front Door.
          • Microsoft Graph for M365 (users, groups, files, calendars).

          Other clouds

          • AWS/GCP services accessed through API connectors or private routing.
          • Consistent audit schema and egress policies across providers.
          Third-Party Integrations

          Business apps

          • CRM/ERP/HR/Finance platforms (e.g., Dynamics 365, Salesforce, SAP, Workday, NetSuite, HubSpot).
          • ITSM/Support tools (ServiceNow, Jira, Zendesk).
          • Collaboration (Teams, Slack) for message/file ingestion under RBAC.

          Approach

          • Server-side connectors (no browser tokens) with typed inputs/outputs.
          • Per-connector policy packs: rate limits, payload size caps, PII scrubbing rules.
          • Observability: per-call logs, correlation IDs, metrics (latency, throughput, errors).
          Custom Connectors

          SDK & Plugin Framework

          • Clean, developer-friendly SDK with dynamic input fields, dependent pickers, and output schemas.
          • Safe execution model (containerized, server-side only); no credentials in code—Key Vault required.
          • Versioned manifests, semantic versioning, and canary rollout support.

          Developer experience

          • Local test harness and contract tests.
          • CI/CD templates for build, sign, scan, and publish to internal catalog.
          • Linting for policy compliance (egress, secrets, PII handling).
          Integration Patterns
          • Event-driven: webhooks/queues trigger Flowise workflows; sub-second SLA where supported.
          • Batch: scheduled extractions, transformations, and vectorization.
          • CDC: stream updates into search/vector stores; reconcile with source-of-truth keys.
          • Federated read (“zero-copy”): query on demand with source permissions; cache where allowed.
          • RAG with metadata filters: retrieve only documents the caller is entitled to see.
          • Human-in/on-the-loop: approvals, exception handling, and reversible actions.
          Data Synchronization

          Strategies

          • Full load → incremental: initial backfill, then delta by timestamp or CDC.
          • Key-based upsert: stable IDs map to source objects; conflict resolution via version/ETag.
          • Schema evolution: additive fields handled automatically; breaking changes flagged in CI.

          Quality & lineage

          • Checksums and record counts per batch; drift detection vs. source.
          • End-to-end lineage: source → transform → index/vector → consumer workflow.
          • Rollback plans and replayable jobs from durable queues.

          Governance

          • Data minimization and retention policies per dataset.
          • PII/PHI tagging, masking, and allow/deny lists enforced at connector and query-time.
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          Complementing Microsoft, Not Competing

          Microsoft delivers powerful AI services such as Microsoft Copilot, Azure OpenAI, and Azure AI Search. These are essential building blocks — but by themselves, they do not provide the full enterprise AI fabric.
          AI Fabrix is not a competitor to Microsoft. Instead, it complements Microsoft by adding the missing enterprise control layer: governance, metadata-aware retrieval, and predictable economics — all running in your own Azure tenant.

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          Why This Matters

          - Copilot is user-facing: It provides productivity features inside Office, Teams, and Dynamics, but it is limited to those applications.

          - Fabrix is platform-level: It governs, orchestrates, and secures all enterprise AI workloads across Microsoft 365 and beyond.

          This distinction allows enterprises to use Copilot for personal productivity while relying on Fabrix for governed, organization-wide AI adoption.

          The Work Gap Without Fabrix

          Many CIOs report that using Copilot alone requires a lot of manual work:

          - Custom connectors must be coded and maintained separately.
          - Compliance, audit, and quota enforcement are missing.
          - No central governance across AI pipelines.
          - Cross-system knowledge retrieval needs heavy custom development.

          Fabrix fills these gaps out of the box, turning Microsoft’s AI services into an enterprise-ready platform.

          Complementing Microsoft
          Feature / Dimension
          Microsoft Copilot
          AI Fabrix (Inside Azure)
          Deployment
          Microsoft CopilotSaaS, hosted by Microsoft
          AI Fabrix (Inside Azure)Runs fully inside customer’s Azure tenant (no external SaaS)
          Scope
          Microsoft CopilotProductivity apps (Word, Excel, Outlook, Teams, Dynamics)
          AI Fabrix (Inside Azure)Enterprise-wide platform: connectors, RAG pipelines, multi-app integration
          Data Control
          Microsoft CopilotData flows through Microsoft SaaS services
          AI Fabrix (Inside Azure)Data stays in customer’s tenant, stored in Azure resources (Key Vault, VNet, Storage)
          Identity & Permissions
          Microsoft CopilotTied to Microsoft 365 apps; limited cross-system inheritance
          AI Fabrix (Inside Azure)Entra ID integration, SCIM, RBAC across apps, connectors, and custom workflows
          Retrieval (RAG)
          Microsoft CopilotPre-built, app-specific retrieval only
          AI Fabrix (Inside Azure)Metadata-aware, policy-aware retrieval across SharePoint, Teams, CRM, ERP, DBs, files
          Governance & Compliance
          Microsoft CopilotBasic tenant settings, but no AI-specific policy packs
          AI Fabrix (Inside Azure)Full governance: audit trails, quotas, policy-as-code, ISO-27001 aligned
          Customization & Extensibility
          Microsoft CopilotLimited to Microsoft app ecosystem
          AI Fabrix (Inside Azure)SDK & plugin framework; extensible connectors and workflows; no vendor lock-in
          Observability
          Microsoft CopilotMinimal insights into usage/cost
          AI Fabrix (Inside Azure)Centralized logs, metrics, traces, cost telemetry, correlation IDs
          Economics
          Microsoft CopilotPer-license SaaS subscription
          AI Fabrix (Inside Azure)Predictable tiers + direct Azure billing (transparent cost control)
          Use Case Fit
          Microsoft CopilotIndividual productivity boost
          AI Fabrix (Inside Azure)Enterprise AI fabric: business cases, policy-aware assistants, cross-system orchestration

          Use Cases

          AI Fabrix enables enterprises to build secure, policy-aware AI solutions on top of Microsoft services. Its in-tenant design, metadata-aware retrieval, and governance features make it ideal for scenarios where compliance and business value must go hand in hand.

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          Microsoft 365 Knowledge Retrieval

          Fabrix integrates with SharePoint, Teams, and OneDrive to deliver permission-aware retrieval. Users only access the documents and messages they are entitled to, reducing compliance risks.

          This allows organizations to create internal knowledge assistants that scale across departments while maintaining security and access integrity.

          Policy-Aware Assistants

          OpenWebUI provides a space for teams to build cases collaboratively. Users can attach evidence from SharePoint, Teams, CRM, or ERP systems, while Fabrix enforces audit logging and access controls.

          This turns conversational AI into a secure workspace for compliance-driven processes like investigations, audits, and legal reviews.

          Unlike generic chatbots, Fabrix enables the creation of assistants with built-in policy enforcement. These assistants apply enterprise rules to every interaction, ensuring outputs comply with governance, security, and regulatory standards.

          They are particularly valuable in regulated industries such as finance, healthcare, and the public sector.

          Secure Case Building & Collaboration
          Sales & Project Workspaces

          Fabrix can support sales and delivery teams by creating workspace assistants linked to deals or projects. These workspaces aggregate documents, meeting transcripts, and evidence, making it easier to collaborate and prepare business cases.

          Integration with CRM systems ensures that AI-powered insights are contextualized and policy-aware.

          Generic Chatbots vs. Fabrix Use Cases
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          This diagram shows the contrast:

          Generic chatbots answer questions but lack governance, policy, or compliance guarantees.

          Fabrix use cases are structured, governed, and directly tied to enterprise processes.

          Use Cases — Summary & Enterprise Outcomes

          AI Fabrix extends Microsoft 365 and Azure with enterprise-ready AI use cases that are secure, policy-aware, and directly tied to business outcomes.
          By combining metadata-aware retrieval, governance enforcement, and collaborative workspaces, Fabrix moves AI adoption beyond pilots and into production-grade solutions.