Client

eFraud Services
A specialized fintech / reg-tech company providing AI-powered fraud detection for financial documents used by federal agencies, major banks, and compliance institutions.

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Challenge

For five years, eFraud Services attempted to build an AI-driven fraud detection platform capable of analyzing tax documents, bank statements, W-2s, paystubs, and high-risk financial submissions. Multiple development teams had tried and failed to deliver a working, scalable product.

The problems included:

• An unstable and incomplete API
• A fragmented architecture that couldn’t scale
• Broken or unreliable AI document-processing workflows
• No CI/CD pipeline, making deployments slow and error-prone
• A cloud environment (AWS) that was expensive, unstructured, and difficult to manage
• A product that could not be demonstrated reliably to enterprise clients

The company was stalled — five years in, with no production-ready platform, losing potential major contracts and credibility.

They needed someone who could come in, solve the core engineering problems, and deliver a complete, production-grade platform fast.

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Solution Delivered

In 60 days, I completely transformed the entire platform — turning a non-functional prototype into a fully operational fraud detection system used today by federal agencies and major financial institutions.

1. Rebuilt the API From the Ground Up

• Designed a clean, scalable, and high-performance C#/.NET API
• Added validation layers, security, and input normalization
• Implemented asynchronous document-processing workflows
• Stabilized the entire system so it could support real-world enterprise workloads
• Created clear documentation and onboarding paths for their clients

This replaced years of patchwork code with a modern, maintainable platform.

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2. Re-architected Their AWS Infrastructure

I took their scattered cloud environment and transformed it into a robust, scalable, and cost-controlled architecture.

Key improvements included:

• Modernized compute using ECS/EKS and right-sized EC2 instances
• Implemented S3 lifecycle management and secure storage patterns
• Added proper IAM roles, policies, and least-privilege design
• Redesigned their VPC structure for performance and security
• Implemented auto-scaling for peak document-processing times
• Reduced waste and optimized overall spend

The result was enterprise-grade stability that could support federal workloads.

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3. Built End-to-End CI/CD

The team previously deployed manually, resulting in:

• Downtime
• Inconsistent builds
• Missing dependencies
• Slow iteration cycles

I implemented a modern CI/CD pipeline with:

• Automated builds
• Automated tests
• Automated deployments
• Environment promotion workflows
• Versioning and rollback capability

This dramatically improved release frequency, stability, and confidence.

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4. Completed the Entire Product in 2 Months

What multiple teams could not solve in five years, I finished in ~60 days, including:

• Full fraud detection workflow
• AI document analysis pipeline
• Confidence scoring, anomaly detection, and result formatting
• User-ready API endpoints
• Document preprocessing logic
• Dashboard integrations
• Production deployment
• Logging, monitoring, and alerting
• Full testing suite

The platform went from “non-functional prototype” to “enterprise-ready system.”

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Results

🔹 Completed Platform After 5 Years of Failure

The company had gone through multiple agencies, developers, and consulting firms — but no one could deliver a working product.
You solved it in two months.

🔹 Enterprise-Grade Stability & Predictability

The system now supports:

• Federal agencies (including the FBI)
• The IRS
• Multiple major banks
• Large financial institutions

🔹 Scalable Cloud Architecture

Auto-scaling, stable deployments, and predictable costs allowed the company to onboard high-volume clients without downtime.

🔹 CI/CD = Faster Innovation

The team can now:

• Release features quickly
• Deploy safely
• Test automatically
• Expand the platform with confidence

🔹 Company Transformation

This engineering turnaround is the foundation that allowed eFraud Services to become a profitable, fully operational company with top-tier clients.

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Technologies Used

• C# / .NET
• AWS (EC2, S3, ECS/EKS, Lambda, IAM, CloudWatch)
• AI document processing frameworks
• Git-based CI/CD pipelines (GitLab/GitHub Actions)
• Event-driven microservice architecture

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Impact Summary

In just 60 days, I rebuilt eFraud Services’ entire fraud detection platform — something multiple firms had failed to do over five years. The result is a fully operational, AI-powered system now trusted by the FBI, IRS, and major banks. With a modern API, scalable AWS infrastructure, and robust CI/CD, the company is now positioned for long-term growth, enterprise expansion, and federal-level reliability.