Case Study

AI Auditor

Intelligent Document Parsing for Financial Audits: Designed to read complex files, extract key data, and reduce audit effort by 80%

A leading audit firm approached Galific with a challenge: they had accumulated thousands of financial documents, scanned PDFs, Excel sheets, receipts, invoices, and ledgers, all in inconsistent formats. Their teams spent countless hours manually processing these files, especially during peak audit season. Before promising any solution, we proposed a data audit to assess whether automation could realistically handle their document diversity and extraction accuracy requirements.

The audit revealed critical intelligence: their documents, despite being messy and unstructured, followed predictable patterns in key field locations, naming conventions, and data hierarchies. We identified that 85% of their document types could be reliably parsed using trained AI models. With this confirmation, we knew we could build AI Auditor, a platform that automates the entire intake process at scale

We helped design the platform’s:

NLP and OCR-powered document reading engine Multi-format ingestion pipeline (scanned PDFs, Excel, JPGs, handwritten notes) Intelligent field extraction system for payer/payee, amounts, dates, account codes Customizable output templates for immediate audit workflow integration

Key Highlights

01 Designed a custom AI system to parse scanned and unstructured financial files
02 Trained models to recognize patterns in varied formats bills, balance sheets, receipts
03 Built an interface to upload documents in bulk and get structured spreadsheets
04 Eliminated the need for manual copy paste and format rework
05 Helped audit firms process 10x more documents without adding headcount
06 Reduced audit prep time by over 70%
07 Improved accuracy and consistency in extracted data
08 Enabled firms to deliver faster reports and improve client satisfaction
09 Compliant with finance-grade data handling and privacy best practices

At Galific, we understand how repetitive, error prone, and time consuming document processing is for financial professionals. AI Auditor was built to solve this at scale. We combined NLP, OCR, and pattern recognition to create a system that reads financial documents the way humans do but faster, and without mistakes.

The Challenge

Audit teams struggled with large volumes of financial records in different formats. Manually opening, reading, and extracting key data slowed down the audit process and introduced human error, especially under tight turnaround times.

The Audit-First Approach

Our initial audit of their historical documents revealed that while formats varied widely, the information architecture remained consistent. We tested extraction accuracy across 500+ sample documents before committing to full development. The data proved that AI-driven parsing could achieve 95%+ accuracy, making automation not just possible, but reliable.

The Solution

Intelligent Document Reading

We developed a robust engine that reads varied formats (scanned PDFs, Excel, JPGs) and uses trained AI models to extract key fields, payer/payee names, amounts, dates, account codes, and invoice references.

Instant Data Structuring

The tool feeds extracted results into customizable spreadsheet templates. Clients get clean, organized data that's instantly ready for review, analysis, or report building.

Pattern Recognition

AI models identify document types automatically and apply the right extraction logic, no manual sorting required.

The Results

80% Reduction in Document Processing Time - What took 3-4 hours per batch is now completed in minutes. Teams shifted focus from data wrangling to actual audit analysis and insights.

95%+ Extraction Accuracy - AI-led parsing reduced inconsistencies and helped auditors deliver more reliable findings. Manual verification time dropped by 70%.

Scalability During Peak Season - Audit spikes during financial year-ends are now handled without hiring seasonal staff. The platform processed 50,000+ documents in a single quarter

Faster Turnaround for Clients - Report delivery timelines improved by 40%, strengthening client relationships and enabling the firm to take on more engagements.

Zero Training Required - New audit staff became productive on day one. The intuitive interface eliminated the learning curve for document intake.

Enhanced Compliance Tracking - Automated extraction ensured no key fields were missed, improving audit trail completeness and regulatory compliance.

Cost Savings of ₹18L Annually - Reduced dependency on manual data entry staff and seasonal hires translated to significant operational cost savings.

Conclusion

AI Auditor is a practical, high-impact tool that brings automation into a domain that's long relied on manual processes. Galific's audit-first approach ensured we only built what the client's data could reliably support. The result: a system that streamlines operations, reduces risk, and scales confidently, proving that even traditional sectors like audit are ready for intelligent transformation.