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Case study · FinTech / Payment Processing
AI document intelligence for invoice and bank statement workflows.
OCR-driven agents that read, classify and reconcile invoices and bank statements for a high-risk payments provider, cutting processing time by around 80%.
- Client
- RevitPay
- Industry
- FinTech / Payment Processing
- Delivery
- SaaS document intelligence platform
- Capability
- Document AI
Results at a glance
~80%less document processing time
The challenge
What RevitPay was up against.
Operating in high-risk payment verticals, finance teams had to reconcile large volumes of invoices and statements by hand, with brittle automation that broke on format changes.
- Manual data entry from PDFs and scans
- Automation that broke with format variety
- Long processing and reconciliation cycles
The solution
What we built.
Skugal built a document intelligence platform whose agents classify, extract, normalise and reconcile financial documents and feed existing payment workflows.
OCR extraction agents
Textract and Google Cloud Vision extract invoice fields, line items, transactions, balances and references into structured records.
Reconciliation insights
Invoice-to-payment matching, reconciliation reports and ERP-ready exports.
Serverless scale
Event-driven Lambda processing with fault-tolerant queues for peak loads.
How it works
From input to outcome.
- Document upload
- Classification agent
- OCR extraction agent
- Validation & normalisation agent
- Reconciliation & reporting agent
- Dashboard, export & API
Business impact
What changed.
RevitPay launched a production-grade document platform that reduces overhead and improves financial data accuracy across its payment operations.
Technology
- AWS Textract
- Google Cloud Vision
- AWS Lambda
- Queue-based async jobs
- CSV / Excel export
- ~80% reduction in document processing time
- Significant drop in data entry errors
- Faster reconciliation and reporting
- Real-time visibility into payment lifecycles
Payments move fast. Financial document workflows should keep pace.
Next step
Have a similar challenge?
Tell us about the workflow or product. We’ll come back with an architecture, a delivery plan and where AI agents fit.