TrueMorph - AI-Native Data Modernization Platform

TrueMorph is an AI-native, cloud-scale data transformation and GenBI platform built on Microsoft Fabric. It helps enterprises convert raw, fragmented data from systems such as Dynamics 365, ERP, CRM, operational databases, and business applications into trusted, analytics-ready, and AI-ready datasets using a Lakehouse architecture with Bronze, Silver, and Gold layers.

The platform embeds Generative AI across the ETL lifecycle to support automated validation, anomaly detection, rule generation, and self-healing pipelines that address schema drift, type mismatches, null handling, and data quality issues. With PII/PHI discovery and masking, lineage, audit trails, human-in-the-loop approval gates, AI Readiness Scoring, and built-in GenBI capabilities, TrueMorph helps enterprises modernize their data estate, improve trust in analytics, and prepare data for BI, machine learning, and Generative AI at scale.

Offer Deliverables

TrueMorph delivers a reusable, enterprise-ready platform for modernizing data estates on Microsoft Fabric, including:

1. AI-Native Data Modernization Platform - A reusable foundation for transforming raw enterprise data into trusted, analytics-ready, and AI-ready datasets using Microsoft Fabric, Databricks, and Azure-native services.

2. Fabric Lakehouse and Medallion Architecture - Support for Bronze, Silver, and Gold data. layers to organize, validate, transform, and publish enterprise data for analytics and AI workloads. Microsoft Fabric supports medallion architecture patterns for lakehouse modernization and optimized analytics.

3. AI-Driven Data Ingestion and Transformation - Generative AI-assisted ingestion, schema inference, transformation logic, and validation across data layers to reduce manual ETL development and accelerate modernization.

4. Automated Anomaly Detection and Rule Generation - Continuous monitoring of data patterns to detect anomalies such as schema drift, freshness issues, volume drops, and distribution shifts, with reusable monitoring rules generated and refined over time.

5. Self-Healing Data Pipelines - AI-generated PySpark transformations to address schema mismatches, type casting issues, null handling, and data drift, helping reduce pipeline failures and manual firefighting.

6. Data Quality and Profiling Controls - Automated data profiling and validation to assess freshness, distribution, null rates, schema consistency, and data quality during ingestion and transformation.

7. AI Readiness Scoring - Continuous scoring across Bronze, Silver, and Gold layers based on data quality, completeness, schema stability, bias risk, lineage completeness, masking coverage, and governance compliance.

8. PII/PHI Discovery and Masking - Automatic discovery and compliant masking of sensitive data across layers, with audit trails to support regulatory, privacy, and enterprise security requirements.

9. Human-in-the-Loop Gold Layer Controls - Business and compliance validation workflows before curated Gold datasets are published for BI, GenBI, downstream AI, or production consumption.

10. Governance, Lineage, and Security Integration - Integration with Microsoft Purview, Azure Active Directory, Azure Key Vault, Unity Catalog, and lineage controls to support governed data flow, access control, secrets management, and compliance. Microsoft Purview and Microsoft Fabric work together to support governed data flows across the enterprise.

11. GenBI and Trusted Analytics Layer - Power BI-like analytics with natural language querying, AI-generated insights, automated aggregations, and curated Gold-layer datasets for BI tools and business dashboards.

12. Multi-Use Case Enablement - A platform baseline that supports data quality modernization, secure analytics, AI readiness assessment, governed GenBI, compliance monitoring, and trusted data activation for downstream BI, ML, and Generative AI workloads.