PRODUCTION LEGAL AI · WORKFLOW ENGINEERING

From First Contact to Attorney Review: Human-Governed AI Intake

From client intake and document analysis to workflow orchestration and approval systems: an AI-assisted workflow that connects first contact to human review.

This case study abstracts client-sensitive implementation details. The system flow uses no client data or product screenshots.

The engineering problem

Intake is a chain of operational decisions across conversations, messages, documents and case information. The system needs to organize that context for review while keeping permissions and human responsibility explicit.

My engineering scope

Architecture and implementation: Full-stack intake experiences and the services behind the workflow.
AI and workflow orchestration: AI-assisted workflow logic with explicit human review boundaries.
Backend and processing: Node.js, FastAPI and background document processing.
Enterprise integrations: Microsoft Graph, SharePoint and supporting APIs.
Governance: Permissions, approval controls and action-level auditability.

Simplified system flow · no client data

Input channels
Voice · Email · Documents
Workflow / orchestration
Intake · AI assistance · Background processing
Context
Case data · Documents · Knowledge / retrieval
Human governance
Permissions · Review · Approval
Controlled execution
Approved actions · Notifications / updates
Auditability
Action logs · Workflow state · Review trail

How the system fits together

Voice and email intake

AI-assisted new-client intake brings voice conversations and email into the workflow. Real-time transcription supports caller qualification and captures context for review.

Documents and case context

Document intelligence supports understanding, extraction and classification. Case and client metadata provide structured context for the people reviewing the intake.

Review before action

Conflict review workflows, suggested next actions and attorney or case-manager review keep consequential decisions with people. AI recommendations are separate from execution.

Connected business systems

Microsoft Graph and SharePoint integrations connect email and documents to the workflow. Permission-aware access governs the information available to the AI system.

Processing outside the request

Node.js and Python/FastAPI services support the application and processing layers. Large files run through background processing, alongside scheduled and priority workflows.

Accountable execution

Human approval precedes consequential execution. Controlled tools and action-level audit trails make the approved action and its execution reviewable.

The boundary between suggestion and action

The AI can organize intake information and propose next steps. Conflict review and consequential actions stay within attorney and case-manager review workflows. A recommendation is not permission to execute.

The production capability is the connected workflow: intake, structured context, document intelligence, human review and controlled execution with an audit trail.

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