Our client is a prominent US payment processor that handles underwriting, onboarding, risk assessment, billing, and merchant funding entirely in-house. Decades of work in payment processing have built long-standing merchant relationships that depend on reliable, accurate transaction handling.
At roughly 15 million transactions processed annually, reliability and penny-level accuracy are not optional. They are the foundation of merchant trust and the client's competitive position in the market.
Intronsoft had already supported this client for more than four years before the back-end rebuild began. That shared history meant the team understood domain constraints, compliance expectations, and operational rhythms before the first architecture decision was made.
The outcome was a rebuilt AWS-native back-end that supports 10× transaction volume, holds monthly cloud spend under $2,000, and maintains penny-level accuracy, delivered within three months of project initiation.
The challenge
Payment processing for this client operates under fixed processing durations for every transaction. Even the smallest deviation from expected accuracy can lead to financial discrepancies and merchant dissatisfaction. Penny-level precision was non-negotiable.
Business growth required an architecture that could absorb a tenfold increase in transaction volume without another full re-architecture cycle. Horizontal and vertical scalability had to be built in from the start, not bolted on after load tests failed.
At the same time, rising cloud expenditure had to come down while service quality stayed the same. The client needed operational simplicity, automated growth paths, and a platform that could scale without a bigger bill every quarter.
Scalable architecture: the solution
The teams aligned on a container-first approach: deploy all application services on AWS ECS Fargate, a highly scalable container service that supports elastic horizontal and vertical scaling without the overhead of managing cluster infrastructure directly.
Redis became a strategic layer in the stack. Essential data, including lookup information, settings, and aggregated historical records, was preloaded into Redis Cache to accelerate retrieval and reduce load on downstream components. Redis also served as the message queue, eliminating the need for a separate queue service and reducing both system complexity and cloud cost.
Between the Java back-end services and the Redis layer, the team applied business logic to batched data flowing through the pipeline. Built with Spring Boot and Apache Camel, this processing path enabled real-time, accurate transaction handling that met the client's penny-level requirements at scale.
Fully processed data was stored in Amazon OpenSearch, giving operations and analytics teams queryable access to transaction records. Front-end teams continued delivery on Angular and Node.js against the same APIs. Together, the stack balanced accuracy, scale, and cost in one coherent architecture.
Implementation journey
To manage the architecture migration, Intronsoft assembled a cross-functional team: a system architect, two back-end developers, two front-end developers, a DevOps engineer, a QA engineer, and a project manager.
Intronsoft engineers embedded alongside the client's in-house team, building on four years of shared context rather than starting cold. The client retained domain ownership of payment workflows and merchant operations; Intronsoft owned the rebuilt back-end architecture, Redis and OpenSearch integration, and DevOps automation.
Delivery focused on incremental validation of processing accuracy, load testing toward 10× volume targets, and continuous cost monitoring against the $2,000 per month goal. Docker and Kubernetes patterns supported consistent environments from development through production on AWS ECS.
Automated deployment and scaling were built in from the start so future growth would not require another manual migration. Daily collaboration and aligned timelines kept both teams on one delivery clock through production cutover.
Business impact
Merchants and internal operations teams gained a platform that meets strict processing windows under significantly higher load. Processing SLAs that once constrained growth now hold at volumes the previous architecture could not have sustained.
Finance and leadership gained predictable cloud economics on a footprint designed for 10× growth. Infrastructure spend became a known quantity rather than a variable that scaled faster than revenue.
Engineering gained a simpler operational model: fewer moving parts with Redis serving as both cache and queue, containerized deploys through ECS Fargate, and automated scaling patterns that reduce manual intervention as merchant volume increases.
Results
The collaboration delivered measurable outcomes within three months of project initiation.
The rebuilt platform processed 2 million transactions in 16 minutes end-to-end, demonstrating throughput at the new scale. Monthly cloud costs were held under $2,000 on infrastructure sized for 10× volume headroom. Penny-level accuracy was achieved and maintained across the migration.
Automated deployments and scaling were in place at launch, giving the client a sustainable foundation for ongoing growth without another architectural overhaul.
Looking ahead
The rebuild positions the processor to grow transaction volume without repeating the migration effort. Automated deploy and scale patterns reduce operational drag as merchant volume increases.
The partnership demonstrates how a long-running engagement plus a focused three-month rebuild can ship production outcomes under strict fintech constraints. Embedded partner engineers, clear ownership boundaries, and an AWS-native stack gave the client room to grow on infrastructure that stays predictable and cost-efficient.