Data & Machine LearningSTATUS: Live 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 ready for ERP ingestion.

Inspect GitHub Source Code

01 // THE BUSINESS PROBLEM & SITUATION

Operational Context

Finance departments were wasting hours manually typing invoice line items into accounting software, incurring data entry errors.

CLIENT / ENVIRONMENT: Targeted at mid-market accounting teams processing hundreds of diverse vendor invoices monthly.

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 strict financial JSON schemas.

invoice-data-extractor-ai.sysData & Machine Learning

VERIFIED ARCHITECTUREAI Invoice Parser

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
Prompt-engineered multimodal vision extraction
Implemented JSON schema validation logic
Created ERP integration webhooks

Key Capabilities Built

✓ PDF & Image Extraction

✓ Granular Line Item Parsing

✓ Per-Field Confidence Scoring

✓ Automated ERP Delivery

DATA_PROVENANCE_TIMELINE // AUDIT_TRACETRACE_ID: #TR-992041

14:22:01.002Source Ingestion

Received webhook from lead submission formOK

14:22:01.045Schema Sanitization

Pydantic validated 12 extracted fieldsOK

14:22:01.210LangGraph Reasoning

Score intent = 0.94 (Tier A Lead)OK

14:22:01.350Human Audit Check

Confidence threshold passed (>0.85)PASSED

14:22:01.480CRM Commit

Created contact ID #884920 in PostgreSQLCOMMITTED

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 ERP.

05 // ENGINEERING LESSONS & OUTCOMES

Technical Challenges Overcome

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

Measurable Outcomes

→ Cut document parsing time by over 80%.

→ Eliminated template brittleness inherent in legacy coordinate OCR.

Lessons & Engineering Rules

"Multimodal models outperform template OCR for unstructured financial layouts."
"Per-field confidence scores enable targeted human review only when needed."

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PROJECT METADATA

ARSLAN'S ROLEMachine Learning Engineer

YEAR & STATUS2024 // Live

CATEGORYData & Machine Learning

TECHNOLOGY STACK

JavaScriptNode.jsVision AIExpressOCR

GITHUB METRICS

Stars: 1

Forks: 0

Verified GitHub Update: 7/17/2026

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