Data & Machine LearningSTATUS: Open Source 1 0

AI Invoice Parser

Structured financial document extraction pipeline

Utilizing multimodal Vision AI models, this system parses varied vendor invoices (PDFs, scans) and converts raw document pixels into typed JSON structures.

Inspect GitHub Source Code

01 // THE BUSINESS PROBLEM & SITUATION

Operational Context

Finance departments often spend hours manually typing invoice line items into accounting software.

CLIENT / ENVIRONMENT: Targeted at accounting teams processing diverse vendor invoices.

02 // SYSTEM ARCHITECTURE & SOLUTION

What Was Built

Built a Node.js microservice that accepts invoice uploads, passes them to a Vision AI model, and maps extracted entities against financial JSON schemas.

INVOICE.EXTRACTOR // VISION_PIPELINESystem Architecture Diagram

UPLOAD

PDF / Scan

VISION AI

OCR Model

VALIDATE

JSON Schema

DISPATCH

ERP Webhook

SYSTEMS_ARCHITECTURE_EXPLODER // LAYERED_STACKFULL-STACK SYSTEM DECOUPLING

Frontend Layer

Next.js 16 App Router, React 19, Tailwind CSS, TypeScript

API & Middleware

FastAPI, Pydantic Schema Validation, Rate Limiters

Orchestration

LangGraph Multi-Agent Supervisor & State Machines

Model Providers

OpenAI GPT-4o, Anthropic Claude 3.5, Vision AI

Data Stores

PostgreSQL (Prisma), Qdrant Vector Store, Redis Caching

Observability

LangSmith traces, Pydantic audit logs, Error retries

03 // ENGINEERING CONTRIBUTION & FEATURES

Exact Contribution

Built the secure document upload API
Engineered multimodal vision extraction prompts
Implemented JSON schema validation logic
Created ERP integration webhooks

Key Capabilities Built

✓ PDF & Image Extraction

✓ Granular Line Item Parsing

✓ Per-Field Confidence Scoring

✓ Automated Webhook Delivery

DATA_PROVENANCE_TIMELINE // AUDIT_TRACETRACE_ID: #TR-992041

14:22:01.002Source Ingestion

Received webhook payloadOK

14:22:01.045Schema Validation

Pydantic structured field checkOK

14:22:01.210LangGraph Reasoning

Evaluated criteria rulesOK

14:22:01.350Human Audit Check

Confidence threshold evaluationPASSED

14:22:01.480Database Commit

Created audit record in PostgreSQLCOMMITTED

*Illustrative system trace — demonstration data, not a client result.

04 // EXECUTION PIPELINE

Step-by-Step Workflow

STEP 01Invoice document is uploaded.

STEP 02Vision AI extracts text and visual structure.

STEP 03Schema validator checks extracted line items.

STEP 04Validated JSON is dispatched to target endpoint.

05 // ENGINEERING LESSONS & OUTCOMES

Technical Challenges Overcome

• Handling low-resolution or skewed scanned documents.
• Parsing varying layout formats without pre-defined templates.

Measurable Outcomes

→ Accelerated document extraction turnaround.

→ Reduced dependence on coordinate-based template OCR.

Lessons & Engineering Rules

"Multimodal models offer greater flexibility than fixed-coordinate template OCR for varied layouts."
"Per-field confidence scores enable targeted human review when needed."

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PROJECT METADATA

ARSLAN'S ROLEMachine Learning Engineer

YEAR & STATUS2024 // Open Source

CATEGORYData & Machine Learning

TECHNOLOGY STACK

JavaScriptNode.jsVision AIExpressOCR

GITHUB METRICS

Stars: 1

Forks: 0

Verified GitHub Update: 7/17/2026

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