Project Background
FinAIx was born out of the need to modernize accounting workflows for SMEs and audit firms. Manual entry of financial documents like invoices, payment receipts, and bank statements wastes hours—and often leads to errors.
We set out to automate the grunt work using AI and intelligent document processing. With FinAIx, uploaded files are scanned using OCR, important fields are extracted and mapped, and clients can approve the entries before accountants process them. From there, financial reports are generated with just a few clicks.
It’s a leap forward from spreadsheets and shared folders—a full system built to save time, reduce errors, and streamline the accountant–client collaboration model.
An AI-powered accounting tool to automate invoice processing, approvals, and financial reporting for modern businesses.
Objectives
Automate Financial Workflows: Use OCR and AI to reduce manual work.
Enable Client-Accountant Collaboration: Build approval layers for transparency.
Generate Reports Easily: Export PDFs and CSVs with one click.
Ensure Data Accuracy: Map key invoice and bank details correctly.
Key Features Delivered
OCR Engine: Reads PDFs and extracts structured invoice data.
Approval Workflows: Clients review before accountants proceed.
Bank Reconciliation Module: Match payments with records.
Dashboard View: Track invoices, approvals, and financial summaries.
PDF & CSV Export: Generate statements with ease.
Our Approach
Discovery Phase:
Mapped accounting pain points and automation opportunities.
Benchmarked against Zoho Books, QuickBooks, and Xero.
Design & Development Phase:
Built using Django backend and Webflow frontend.
Developed data structure for invoices, bank logs, approvals.
Implemented AI-based mapping for field extraction.
Implementation Phase:
Integrated client portals and accountant portals.
Connected Google Sheets export and real-time API logs.
Secured platform with encryption and audit logs.
Results
90% Time Saved on invoice processing.
100+ Invoices Approved in pilot phase.
Automated Financial Reports with <2% error rate.
High User Satisfaction across internal teams.
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