AI Augmented Analytics Platform powered by Microsoft Fabric and Copilot

Quadrant Technologies

Quadrant helps current Microsoft customers activate and optimize Fabric Copilot within Microsoft Fabric, enabling AI-driven insights and automation for enhanced data workflows.

AI Augmented Platform powered by Fabric and Copilot

Quadrant Managed Services

The AI Augmented Platform is designed to accelerate customers' onboarding to Cloud Data platforms with a focus on accuracy, efficiency, and scalability. This platform leverages advanced AI capabilities to streamline data management, governance, and analytics.

Fabric Adoption Methodology:

Quadrant follows structured methodology to onboard customers onto Microsoft Fabric. It involves the following steps:

  • Assessment
  • Adoption – Follows 4 C’s Framework
    1. Competence
    2. Consultation
    3. Certainty
    4. Customer Satisfaction
  • Acceleration
  • Adherence to Enterprise Scale
  • Aligning with industry-standard solutions with Microsoft Fabric's integrated capabilities.

Key Capabilities

1. Data Ingestion

  • Purpose: Migrate data from various sources and databases to the cloud.
  • Approach: Utilize accelerators to ensure seamless and efficient data transfer.

 

2. Medallion Architecture

  • Purpose: Implement data cleansing, standardization, and governance.

     Components: 

  • Cleansing: Remove inconsistencies and errors from data.
  • Standardization: Ensure data follows a consistent format.
  • DQGP (Data Quality and Governance Platform): Automate data quality checks and governance processes.

 

3. Business Context Models

  • Purpose: Generate business context models based on input datasets.
  • Approach: Use Large Language Models (LLMs) to derive insights and create models that reflect business needs.
https://store-images.s-microsoft.com/image/apps.54128.e6a65715-c110-472d-a6e2-8ac41a10cd9d.f7cbae32-9860-478d-b80a-1f2a31e3cf3d.2f53f65e-9b3c-441f-b063-304561d63117
https://store-images.s-microsoft.com/image/apps.54128.e6a65715-c110-472d-a6e2-8ac41a10cd9d.f7cbae32-9860-478d-b80a-1f2a31e3cf3d.2f53f65e-9b3c-441f-b063-304561d63117