Serverless AI-Powered Document Analyzer
Automated Classification, Extraction & Insights from Unstructured Documents

Business Strategy: Value Stream & PI Planning Readiness

This strategy focuses on transitioning the enterprise from Fragmented Data Silos to an Autonomous Data Ingestion Value Stream. We utilize TOGAF to define the "What" (Architecture) and SAFe to define the "How" (Execution).

1. Value Stream Mapping: The Document-to-Insight Flow

As a SAFe SPC, I have mapped the Development Value Stream to identify a 70% reduction in non-value-added time by moving from manual batching to event-driven serverless triggers.

2. SAFe PI Planning Readiness

To ensure readiness for an Agile Release Train (ART), I developed the following PI Objectives for the Autonomous Multi-Model Ingestion Fabric:

TOGAF Phase E: Strategic Roadmap & Capability Map

This foundational document intelligence service is supported by a clear roadmap and capability views, ensuring strategic alignment across the enterprise:

A. Business View: Document Intelligence Capability Map

Highlighting core Classification and Extraction engines as central business capabilities.

B. Operational View: Value Stream Coordination

Demonstrating how the Document Analyzer feeds critical data into RevRec-AI and ContractGuard ARTs.

C. Roadmap View: 3-Phase Architecture Evolution

Progressing from a "Free Tier POC" to a "Global Enterprise Service" with incremental value delivery.

This roadmap ensures predictable delivery and aligns technical investments with evolving business needs, demonstrating measurable ROI at each phase.

01a. Stakeholder Personas: Eliminating Unstructured Chaos

The Serverless Document Analyzer acts as the "Optical Nerve" of the enterprise, converting raw unstructured data into high-fidelity signals for the downstream agentic ecosystem.

01d. Technical Rollout Roadmap

This implementation roadmap sequences prioritized user stories into SAFe Program Increments (PIs), prioritizing Must-Have ingestion and extraction in Phase 1 to enable immediate unstructured data intelligence. The strategy targets zero-ops scalability before maturing into multimodal enrichment and seamless downstream routing for the broader ecosystem.

This sequencing prioritizes Must-Have stories in Phase 1 to deliver rapid document chaos resolution, enabling quick wins for downstream systems. Under SAFe, each PI includes enabler spikes (e.g., concurrency tuning) and ART coordination for cross-subsystem event contracts, specifically with Contract Guard for parsed feed alignment.

Technical Solution: The Serverless Reasoning Stack

This solution is architected as an Event-Driven AI Orchestration Fabric. It moves beyond simple "classification" into Context-Aware Routing, ensuring that data from a 100-page PDF is segmented and sent to the correct specialized engine (RevRec-AI or ContractGuard) with zero human intervention.

1. The Intelligent Router Agent (CrewAI + LangChain)

Instead of a single "if-else" block, we deploy a Crew of specialized agents running in a Cloud Run container. This swarm performs a semantic "triage" of incoming files:

2. Event-Driven "NoOps" Pipeline

Architecture Lenses: EA Rigor & SAFe Solution Context

To validate enterprise rigor, the solution is decomposed through three architectural lenses, ensuring security, scalability, and cross-train integration.

1. Application (EA): C4 Component Map

Visualizing LangChain reasoning and CrewAI state management layers.

2. Solution (SAFe): System Context

Positioning the Analyzer as a Shared Service across multiple Agile Release Trains (ARTs).

Zero-Trust Serverless Deployment Blueprint

Showcasing VPC-SC and IAP protecting Document AI and Hugging Face endpoints.

Intelligence Platform: The Serverless Data Fabric

The platform is architected as an Autonomous Data Loop. It leverages BigQuery as the "Brain" and Vertex AI Model Monitoring as the "Nervous System" to ensure that Hugging Face and Document AI models perform at enterprise-grade levels.

1. The BigQuery "Intelligence Hub"

Using TOGAF Phase C (Data Architecture), I designed the transition from unstructured "Dark Data" to a structured, queryable Semantic Layer:

2. Model Monitoring & Drift Detection (Vertex AI)

To maintain 95%+ Classification Accuracy, we implement monitoring that treats AI performance as a critical system metric:

TOGAF Phase C: Data Architecture (Lineage & Lifecycle)

Data Journey: Raw GCS Ingestion to R Shiny Insights

EA Viewpoint: Visualizing the end-to-end data lifecycle. This ensures auditability by tracing every business insight back to the original document upload in Cloud Storage.

MLE View: Vertex AI CI/CD/CT Pipeline (Retraining Loops)

Continuous Training: Automated Model Card & Agent Updates

MLE Viewpoint: Highlighting the automated retraining loop. When model drift is detected, Vertex AI Pipelines trigger retraining, update Model Cards, and hot-swap serverless agents without downtime.

Model Lifecycle (MLE): The Open-Source-First MLOps Strategy

The "Brain" of this analyzer uses fine-tuned Hugging Face models managed through a fully automated CI/CD/CT (Continuous Training) pipeline on Vertex AI. This replaces rigid regex systems with adaptive, LoRA-based intelligence.

1. The Fine-Tuning Strategy: LoRA-Based Specialization

Using LoRA (Low-Rank Adaptation), we fine-tune open-source classifiers on company-specific layouts to achieve 95%+ precision with a minimal compute footprint.

2. The Vertex AI "AutoMLOps" Pipeline (SAFe Ready)

TOGAF Phase H: Explainability (XAI) & Drift Defense Architecture

Continuous Monitoring ensures the "Scale-to-Zero" architecture remains intelligent and compliant even as document layouts and data distributions shift:

Monitor	Metric	Strategic Action
Feature Drift	K-S Test	Alerts team if incoming PDF structures/OCR confidence shift significantly.
Concept Drift	Accuracy %	Triggers automated CT (Continuous Training) pipeline via Vertex AI.

Explainability: Feature Attribution

Visualizing spatial field importance (e.g., "Total Due") driving classification logic.

MLOps: Lifecycle Handoff

Orchestration flow: Hugging Face Hub → Vertex AI → Cloud Run.

Cloud Infrastructure: The Scale-to-Zero Vault

The infrastructure is architected using a Serverless Hub-and-Spoke model. We leverage Cloud Run and Cloud Functions for compute, wrapped in a VPC Service Controls (VPC-SC) perimeter to ensure document data never touches the public internet during inference.

1. Zero-Trust Security Architecture (EA View)

Using TOGAF Phase D (Technology Architecture), I designed this stack to eliminate implicit trust. Every document processed is an isolated event within a secure context:

2. SRE: High-Availability "NoOps"

We utilize Google’s SRE "Golden Signals" to manage the reliability of a system that technically "disappears" when not in use:

TOGAF Phase D/G: Infrastructure Governance & GitOps Blueprints

High-fidelity architectural views designed to satisfy the Chief Architect and Cloud Engineering Manager by proving sovereign security and automated lifecycle management:

Physical Architecture: VPC-SC Perimeter

Zero-Trust: Containing the serverless event flow within a strict Service Perimeter to prevent data exfiltration.

GitOps: Terraform-Led Governance

Automation: provisioning the stack, IAM policies, and resource quotas automatically via Terraform Cloud.

Sovereign Reliability: By codifying the security perimeter in Terraform, we ensure that every document ingestion event is protected by enterprise-grade encryption and isolation by default.

Governance & SRE: PI Planning & Reliability Engineering

As an SPC, I ensure that the Document Intelligence capability is a governed, reliable component of the broader Solution Intent, rather than a siloed tool.

1. SAFe Governance: PI Planning Readiness

To transition this from a POC to a production asset, I defined the Solution Epic and guardrails for the upcoming Program Increment:

2. SRE: Automated Incident Response (The "Self-Healing" Pipeline)

In a NoOps environment, we rely on automated remediation to maintain our 99.9% Uptime SLO:

TOGAF Phase G: Enterprise Compliance & Security Governance

We implement Least-Privilege Security and SRE Observability as foundational enablers for the enterprise portfolio:

Identity & Secret Governance

Key Management: Automated rotation of Hugging Face & Document AI keys via Cloud Secret Manager.

Binary Authorization & Scanning

Supply Chain: Ensuring only vulnerability-scanned containers execute on Cloud Run.

SRE Performance & Insights Dashboard (R Shiny)

Real-time observability into document latency, request tracing, and success rates.

Traceability: End-to-End Audit Lineage

Impact & Outcomes: Quantifying the Intelligence Dividend

This project transforms the enterprise's relationship with unstructured data, moving from a "reactive batch" mindset to an "active intelligence" model. Success is measured through throughput, cost-efficiency, and executive decision velocity.

1. Throughput & Operational Efficiency

2. Strategic Insight & Accuracy

TOGAF Phase G: Impact Metrics & Strategic Waterfall

Strategic outcomes proven through high-fidelity metrics, demonstrating the transition from legacy manual triage to an AI-First Serverless Fabric:

Speed-to-Value Waterfall (SAFe)

Efficiency: Visualizing the 85% reduction in latency through waste elimination.

FinOps: Cumulative Spend Comparison

Cost Control: Comparing traditional VM overhead vs. the Scale-to-Zero model.

Accuracy vs. Drift Heatmap (MLE View)

Sustaining 95% accuracy via automated retraining loops even as document layouts evolve.