Client retained at $18K/month · Phase 3: AI merchant risk scoring & intelligent payment routing (in progress)
Who we worked with
A US-based payment processing company with approximately 300 employees, handling $150M+ in monthly transaction volume across 2,000+ merchant accounts. The company provides payment gateway services to mid-market e-commerce businesses.
Their core payment platform — built 11 years ago on .NET Framework 4.0 with a monolithic architecture — was reliable but increasingly painful, losing merchants to faster competitors while fraud slipped through manual reviews.
A platform losing ground to faster competitors
The monolithic architecture was creating compounding pain — slow transactions, infrequent deployments, and manual fraud review that missed 35% of fraudulent transactions.
Transaction Latency Killing Merchants
Processing averaged 2.8 seconds — competitors were under 500ms. Merchants were actively threatening to switch providers.
2.8s average latencyHigh-Risk Deployments
Releases happened once every 3–4 weeks, requiring a 2-hour maintenance window at 2 AM with the entire engineering team on call.
3–4 wk deploy cycleManual Fraud Review Falling Short
8 analysts reviewed flagged transactions one by one, catching only 65% of fraud — $2M+ in annual losses slipping through.
65% detection rateManual Scaling for Peak Loads
Black Friday required provisioning extra servers 2 weeks in advance — and the system still degraded above 800 transactions per second.
800 TPS ceilingAging PCI DSS Compliance
PCI DSS Level 1 was maintained, but audits were taking 6 weeks and growing harder as the codebase aged and documentation lagged.
6-week audit cyclesAI Ambitions Blocked by Architecture
The CTO wanted real-time AI fraud detection — but the monolith couldn't support the sub-100ms inference latency required for pre-auth scoring.
Two overlapping phases, zero downtime
A 22-week phased delivery — re-architecting the payment platform while building the AI fraud pipeline simultaneously, with transactions processing throughout the entire migration.
- Decomposed the payment monolith into 11 microservices: transaction gateway, merchant management, settlement, reconciliation, dispute resolution, reporting & more
- Migrated to AWS with auto-scaling: EKS (compute), Aurora PostgreSQL (transactional data), DynamoDB (session state), ElastiCache (hot data)
- Event-driven architecture with Kafka — enables real-time processing and independent service scaling
- New React merchant dashboard — real-time transaction monitoring, settlement tracking, self-service reporting
- Zero-downtime blue-green deployments via GitHub Actions — from 2 AM maintenance windows to multiple daily releases
- End-to-end encryption, tokenization, comprehensive audit logging for PCI DSS Level 1 compliance
- Real-time fraud scoring pipeline — every transaction scored by ML model within 45ms of submission, before authorization
- XGBoost gradient boosting model trained on 3 years of labeled transaction data, using 47 features: amount, device fingerprint, geolocation, velocity, merchant category & behavioral patterns
- Adaptive learning pipeline — model retrains weekly on new labeled data (confirmed fraud + false positives) for continuous improvement
- Fraud analytics dashboard for risk team: real-time fraud rate monitoring, geographic heat maps, merchant-level risk profiles
- Merchant anomaly detection — flags accounts with sudden transaction pattern changes indicating potential compromise
47 engineered features
Legitimate vs. fraudulent
New fraud patterns incorporated
Built for speed, scale & compliance
Results that transformed the business
Across performance, fraud prevention, compliance, and team efficiency — all delivered in 22 weeks with zero transaction downtime.
Transactions
Detection Rate
Losses Prevented
Rate (was 12%)
7 engineers, one cohesive delivery
A specialized team combining payments domain expertise with modern cloud engineering and ML — structured for complete ownership across the full stack.
Solution Architect
Payments domain expertise, microservices design, AWS architecture
Senior .NET Developer × 2
.NET 8 microservices, payment gateway logic, API design
Senior React Developer
Merchant dashboard, real-time monitoring UI, reporting interfaces
ML Engineer
XGBoost fraud model, feature engineering, Kafka Streams scoring pipeline
DevOps / Infrastructure Engineer
Kubernetes, Terraform, blue-green deploys, PCI DSS compliance logging
QA Engineer
End-to-end test automation, performance testing, payment flow validation
What the client said
"We were watching merchants leave because our transaction speeds couldn't compete. i-verve didn't just modernize our platform — they rebuilt our competitive position. The AI fraud detection alone has saved us millions, and our merchants are seeing authorization rates they've never seen before. The fact that they did it all with zero downtime while we were processing live transactions is genuinely remarkable."