Convert raw invoices into real-time spend visibility, overdue risk alerts, and payment prioritization — without manual processing.
InvoiceAI was designed to solve a real operational gap faced by small and mid-sized businesses across the UAE and MENA region.
Many finance teams still rely on manual invoice handling despite:
- VAT compliance requirements (5%)
- Multi-vendor invoice formats (English / Arabic)
- High invoice volumes with limited finance staff
This results in:
- 10–15 minutes lost per invoice
- Late payments and missed discounts
- No real-time visibility into liabilities or cash flow
- Critical data trapped inside PDFs
InvoiceAI acts as a Financial Command Center, bridging the gap between raw invoices and informed financial decisions.
InvoiceAI is an end-to-end AI-driven invoice processing and analytics system that transforms unstructured PDFs into structured, actionable intelligence.
- Ingest PDF invoices (batch supported)
- Extract structured data using LLMs
- Apply business rules and risk logic
- Persist normalized data
- Visualize insights in a real-time dashboard
- Process 50+ invoices simultaneously
- Template-agnostic extraction across layouts
- Built-in validation to reduce extraction errors
Invoices are automatically prioritized:
- Urgent: Overdue or high-value invoices
- Routine: Standard recurring payments
- Paid: Automatically archived
Power BI–inspired Streamlit interface:
- Spend KPIs and liability overview
- Overdue risk alerts
- Vendor-level analytics
- Exportable invoice ledger
Each invoice is enriched with:
- Risk classification
- Recommended next action
- Payment priority context
A quick visual overview of how InvoiceAI transforms raw invoices into actionable financial intelligence.
Real-time visibility into spend, liabilities, and invoice status.
LLM-driven prioritization of invoices based on urgency, value, and due dates.
End-to-End Invoice Processing
From raw PDF ingestion to structured data and dashboard updates.
AI-Powered Action Recommendations
How the system reasons over invoices and suggests next actions.
graph LR
A[PDF Invoices] -->|Ingestion| B[Python ETL Pipeline]
B -->|Text Parsing| C[LLM Client]
C -->|Gemini / Longcat| D[Structured JSON]
D -->|Business Rules| E[Action Engine]
E -->|Persist| F[(SQLite / Database)]
F -->|Query| G[Streamlit Dashboard]
- Multi-model AI architecture to reduce vendor lock-in
- Template-free parsing for layout flexibility
- Action-oriented enrichment for finance teams
- Resilient ETL with retry and backoff
- Enterprise dashboard design principles
git clone https://github.com/YOUR_USERNAME/invoice-ai.git
cd invoice-ai
pip install -r requirements.txt
python scripts/init_db.py
python scripts/extract_ai.py
streamlit run scripts/dashboard.py- WhatsApp / Telegram invoice ingestion
- Vision OCR for scanned documents
- Email inbox monitoring
- Dockerized Azure deployment
- PostgreSQL support
Ubed Ullah
Data Scientist & AI Automation Engineer
🔗 LinkedIn: https://www.linkedin.com/in/ubedullah/
💻 GitHub: https://github.com/Ubed-982
InvoiceAI is a portfolio-grade project showcasing production-ready AI automation, data engineering, and financial analytics.