Auto Recon AI

🚀 Auto Recon AI 💡 Inspiration Manual invoice reconciliation is one of the most time‑consuming and error‑prone processes in finance operations. Organizations lose billions annually due to invoice fraud, duplicate billing, and manual inefficiencies.

We wanted to build an AI-powered system that eliminates manual invoice handling by intelligently extracting, validating, and processing invoices with minimal human intervention.

🧾 What It Does Auto Recon AI is an intelligent invoice automation and reconciliation platform that:

📩 Detects invoices (simulated email scanning)

📄 Extracts structured data from PDFs using AI

🔍 Validates invoices against Purchase Orders

🛡 Performs intelligent risk detection

🏢 Automatically posts validated invoices to ERP

📊 Visualizes results in a real-time dashboard

It transforms invoice processing from a manual workflow into an AI-driven financial intelligence system.

🛠 How We Built It We built Auto Recon AI using:

Streamlit – Interactive dashboard UI

LangGraph – Agent orchestration workflow

Custom AI Agents:

Invoice Extraction Agent

PO Validation Agent

ERP Integration Agent

Email Simulation Agent

PDFPlumber – PDF text extraction

FastAPI (Local version) – Backend orchestration (for local testing)

GitHub + Streamlit Cloud – Deployment

Architecture Flow:

Email / Upload → AI Extraction → PO Validation → ERP Push → Dashboard Visualization

⚠️ Challenges We Ran Into Handling inconsistent PDF text extraction

Regex failures due to formatting differences

Managing nested JSON between backend and frontend

Deployment issues caused by localhost API calls

Ensuring proper Git branch synchronization for deployment

We resolved these by:

Switching to structured extraction logic

Removing backend dependency for cloud deployment

Refactoring workflow execution directly inside Streamlit

🏆 Accomplishments That We’re Proud Of Successfully implemented multi-agent orchestration using LangGraph

Built a fully working end-to-end invoice processing pipeline

Integrated automated PO validation and ERP simulation

Created a deployable cloud-based financial AI system

Designed a realistic enterprise-style dashboard

📚 What We Learned Real-world document parsing is more complex than expected

Cloud deployment requires architecture adjustments (no localhost calls)

Agent-based workflows provide better modularity than linear pipelines

Debugging JSON structures is critical in AI systems

Hackathon success requires both technical depth and polished presentation

🔮 What’s Next for Auto Recon AI Integrating real LLM-based contextual invoice understanding

Adding fraud risk scoring and anomaly detection

Supporting multi-invoice batch processing

Real ERP integration (SAP / Oracle)

Vendor behavior analytics and trend dashboards

Audit trail timeline visualization

Our long-term vision is to evolve Auto Recon AI into a full AI-powered financial intelligence and reconciliation platform for enterprises.

Built With

• agents
• fastapi
• langchain
• python