Legal Ops: AI Mail Processing

National Legal Services Provider — End-to-End Document Automation

Click any step to expand details

Step 1

Document Sources

Scanned mail & email attachments flow into unified intake

What Happens

Incoming legal correspondence arrives via physical mail (scanned) or email attachments. All document types are captured including claim notices, medical records, court filings, and correspondence.

Purpose

Capture all inbound legal documents into a unified digital processing pipeline, eliminating manual intake and ensuring nothing falls through the cracks.

Step 2

Ingestion & OCR Pipeline

Azure Blob Storage → Azure Functions (Tesseract OCR + preprocessing)

Event Grid Serverless Tesseract

What Happens

  • • All documents securely stored in Azure Blob Storage as centralized intake repository
  • • Event-driven Azure Function triggered on upload
  • • Image preprocessing improves scan clarity
  • • Tesseract OCR extracts raw text from scanned documents
  • • Email attachments parsed into structured inputs

Purpose

Provide durable, scalable, and secure storage before processing begins. Convert all incoming documents into machine-readable text for downstream AI processing.

Tech Stack

Azure Blob Storage, Azure Event Grid, Azure Functions, Tesseract OCR

Step 3

AI Classification Service

Custom neural network trained on firm's historical documents

Container Apps AKS Neural Net

What Happens

  • • Custom-trained neural network classifies documents based on historical firm data
  • • Document types: claim notices, medical records, court filings, correspondence
  • • Model runs in containerized environment for scalable inference
  • • Achieves 98% classification accuracy

Purpose

Automatically determine document type and enable accurate routing without manual review. Eliminates the need for staff to manually sort and categorize incoming mail.

Tech Stack

Azure Container Apps / AKS, Custom ML Model, Python/TensorFlow

Step 4

LLM Structured Extraction

Azure OpenAI GPT-4 extracts parties, dates, providers, claim IDs

GPT-4 Azure OpenAI Structured Output

What Happens

The LLM extracts key structured entities:

  • • Parties involved (plaintiff, defendant, attorneys)
  • • Important dates (filing, hearing, deadlines)
  • • Medical providers and facilities
  • • Claim identifiers and case numbers
  • • Case details and relevant facts

Model standardizes outputs into a predefined schema.

Purpose

Transform unstructured legal correspondence into structured, searchable data. Enables automated case matching and eliminates manual data entry.

Tech Stack

Azure OpenAI Service, GPT-4, JSON Schema validation

Step 5

Validation & Normalization

Schema validation, formatting checks, file naming conventions

What Happens

Extracted fields are validated against:

  • • Schema rules (required fields, data types)
  • • Formatting constraints (dates, phone numbers)
  • • Database consistency checks
  • • File naming conventions standardized
  • • Duplicate detection and handling

Purpose

Ensure data accuracy, traceability, and compliance before storage. Prevents bad data from entering the system and maintains data quality standards.

Step 6

Azure PostgreSQL Database

Metadata storage, structured entities, classification labels

PostgreSQL Azure DB

What Happens

  • • Structured metadata and extracted entities stored
  • • Classification labels and confidence scores
  • • Routing metadata and case associations
  • • Full audit trail of document processing
  • • Searchable index for quick retrieval

Purpose

Provide a single source of truth for legal document tracking and workflow automation. Enables reporting, analytics, and historical lookups.

Tech Stack

Azure Database for PostgreSQL, Managed Identity, SSL encryption

Step 7

Automated Case Routing

Event Grid + Rule Engine assigns docs to correct case/attorney

Event Grid Rule Engine

What Happens

Metadata triggers routing rules:

  • • Documents automatically assigned to correct case
  • • Workflows initiated based on document type
  • • Relevant teams and attorneys notified
  • • Priority escalation for urgent matters
  • • Deadline tracking and calendar integration

Purpose

Eliminate manual document sorting and reduce operational bottlenecks. Ensures the right people see the right documents at the right time.

Tech Stack

Azure Event Grid, Custom Rule Engine, Azure Logic Apps

Final Output

Case Management System

Processed documents pushed to firm's existing workflows

What Happens

  • • Processed and structured documents pushed to firm's case management system
  • • Documents attached to correct case file
  • • Metadata populated in case record
  • • Task assignments created automatically

Purpose

Seamlessly integrate automation into existing legal workflows without disrupting operations. Staff continue using familiar tools with dramatically improved data quality and speed.

Monitoring & Operational Dashboard

Azure Monitor + Power BI — Real-time visibility across the pipeline

Metrics Tracked

  • • Volume processed (daily/weekly/monthly)
  • • Classification accuracy rates
  • • Extraction accuracy and confidence
  • • Processing latency (end-to-end)
  • • Error rates and exception handling

Purpose

Provide operational oversight, performance tracking, and continuous improvement visibility. Enables data-driven optimization and quick identification of issues.

Tech Stack

Azure Monitor, Application Insights, Power BI

80%
Manual Reduction
300+
Daily Documents
98%
Classification Acc.
24/7
Automated Ops

Dizzain — Production AI Systems