This repository documents the architecture and implementation of an enterprise-grade document intelligence and automation platform.
The platform enables scalable ingestion, AI-based document extraction, data validation and enrichment, and end-to-end automation of document-driven business workflows.
The repository focuses on architecture, orchestration, processing patterns, and operational readiness, rather than proprietary implementation details.
- Business Context
- My Role
- High-Level Architecture
- Repository Structure
- Data Flow (Summary)
- Technology Stack
- CI/CD & Environments
- Security (High-Level)
- Limitations & Future Improvements
- Lessons Learned
Organizations handle large volumes of documents (PDFs, scanned images, structured and semi-structured files) that traditionally require manual review and validation.
The goals of this platform were to:
- Reduce manual processing effort
- Improve data accuracy and consistency
- Enable scalable and auditable workflows
- Integrate with existing enterprise systems
- End-to-end architecture ownership
- Technology and tool selection
- Design and implementation of data orchestration pipelines
- Databricks-based processing logic (Spark / Python)
- Integration of AI-based document extraction services
- Collaboration with infrastructure, security, and business stakeholders
Core components:
- Document ingestion layer
- Orchestration using Azure Data Factory (ADF)
- Processing and enrichment using Databricks (Spark)
- AI-powered document extraction services
- Structured data storage
- Monitoring, logging, and automation services
📐 Architecture documentation:
System-level design, data flow, and governance:
Orchestration, infrastructure, and lifecycle management:
Processing layer architecture and development practices:
databricks/README.mddatabricks/infrastructure.mddatabricks/development.mddatabricks/keyvault-integration.mddatabricks/code-reference.md
AI-based document extraction and GenAI enrichment:
Operational monitoring and logging:
- Documents and metadata are ingested from enterprise sources or APIs
- ADF orchestrates the end-to-end processing lifecycle
- Databricks validates, transforms, and enriches extracted data
- AI services extract structured information from documents
- Data is stored in structured tables aligned with business models
- Logging tables capture execution status, errors, and metrics
- Automation triggers notifications or downstream actions
🔎 Detailed flow:
- Cloud: Microsoft Azure
- Orchestration: Azure Data Factory
- Processing: Databricks, Apache Spark
- AI Services: Azure Document Intelligence, Azure OpenAI
- Programming: Python
- Storage: SQL-based enterprise data stores
- CI/CD pipelines designed for DEV and TEST
- Parameterized deployments
- Production rollout planned but postponed due to organizational constraints
📦 CI/CD details:
- Managed identities for secure access
- Environment separation (DEV / TEST / PROD)
- Configuration-driven pipelines
- Key Vault integration prepared and documented (not activated in this phase)
🔐 Governance and security details:
- Full CI/CD production lifecycle
- Enhanced observability and alerting
- Advanced document classification
- Human-in-the-loop exception handling
- Document intelligence requires strong data engineering foundations
- Clear separation between orchestration and processing is critical
- AI must be tightly coupled with data quality controls
- Early architectural decisions heavily influence production readiness