Engineering that moved the numbers that mattered.

One of the world’s largest banks relied on the partner’s platform to receive, match, process, and return hundreds of millions of transactions each day between its own operations and correspondent banks worldwide — the system of record for moving money in and out of client accounts.

Banking has traditionally settled this work in overnight batches. At this bank’s volume, compressing settlement into a few hours had become impossible — while its clients demanded real-time visibility into their balances, and the bank saw delivering that as a decisive competitive advantage.

The two most expensive operations in transaction processing — finding an account record and updating it — are both bound by disk I/O, orders of magnitude slower than memory. eProxim proposed a focused proof of concept built on an in-memory data grid to remove that bottleneck entirely.

1. Pre-load the bank’s entire client-account dataset into RAM, making memory — not the database — the system of record.
2. Partition accounts across a grid of processing nodes so work is routed to where the data already lives, minimizing network travel.
3. Process each transaction in place, then persist results to disk asynchronously (write-behind) so durability never blocks throughput.

The company’s model depended on aggregating itineraries, pricing, and availability from an ever-growing pool of suppliers — and, unlike competitors who resell a third party’s database, building and controlling its own. Every supplier exposed a different API, and each new connection added latency: querying suppliers live, one by one, meant a single search could take up to a minute. For a consumer-facing site, that is an eternity.

The data also had to be right. Nothing erodes trust faster than a traveler reaching checkout to find the fare has changed or the product is gone. The company needed accurate, real-time pricing — and the ability to cross-sell and bundle products across suppliers on the fly.

Querying every supplier API in real time was never going to meet these goals. eProxim built the solution on an in-memory data grid, holding the entire travel-product catalog in RAM across load-balanced nodes so any consumer request could be answered instantly.

1. Ingest a snapshot of each supplier’s product data through their API into the grid’s in-memory store.
2. Refresh intelligently: an adaptive scheduler polled high-demand products far more often than quiet ones — keeping data current without hammering supplier APIs.
3. Serve every consumer search from memory, with the grid routing each request to the appropriate node and fulfilling it in place.

The firm’s institutional customers — investment managers, broker-dealers, and custodian banks — depend on timely, accurate reporting for trade activity, settlements, fiduciary oversight, compliance, reconciliation, and tax. Those reports are consumed every day by operations and compliance teams managing billions of dollars in assets.

The existing platform, built up in-house over many years, had reached its limits. As volumes grew, performance declined, development slowed, and technical debt mounted: no true on-demand reporting, limited customer control over scheduling, slow execution, brittle point-to-point integrations across many systems of record, and rising maintenance cost. The firm needed a modern reporting architecture — not another refresh.

Rather than patch the old system, eProxim redesigned and rearchitected the platform around a service-oriented architecture built for scalability, resilience, and long-term maintainability.

1. Build a secure enterprise API layer giving standardized access to multiple internal systems of record — replacing tightly coupled point-to-point integrations with reusable services.
2. Implement a MuleSoft integration layer for data aggregation, workflow orchestration, enterprise scheduling, error handling, and operational monitoring across the reporting pipeline.
3. Introduce JasperReports as the reporting engine — high-performance, self-service, on-demand generation with customer-configurable scheduling and secure delivery.

The operator’s network emitted a relentless stream of usage records — every call, text, and megabyte across millions of subscribers. The partner’s platform mediated those records into billable events, but did so in an overnight batch: raw usage was collected all day and rated only after midnight.

That lag had become a liability — and so had the wiring. Billing, real-time balance, spend alerts, and fraud detection each pulled from the batch on their own schedule through separate, brittle integrations, so subscribers saw stale balances, regulators waited on usage transparency, and fraud like SIM-box bypass could run unchecked for a full day. The operator needed usage captured once and delivered to every system the instant it happened.

The gap was not just speed but wiring: every consumer integrated with the batch on its own. eProxim rebuilt mediation as an event-driven pipeline that captures usage once and delivers it to every downstream system through a single contract.

1. Ingest raw usage from heterogeneous network elements into one partitioned event stream, keyed by subscriber so each account’s records process in order.
2. Normalize and rate each event in flight against the subscriber’s plan and live balance, with an idempotent design so duplicate or replayed records never double-charge.
3. Publish rated events to every consumer — billing, real-time balance, spend alerts, and fraud detection — from one stream, replacing the point-to-point links each had maintained.

The retailer sold the same catalog across its own website, several online marketplaces, and physical stores — but each channel kept its own view of stock, reconciled to the ERP by scheduled syncs that ran every fifteen minutes. Orders, returns, and receipts flowed through a tangle of point-to-point connections between the commerce platform, ERP, warehouse system, and each marketplace.

At scale, the gaps showed. A unit sold on one marketplace could still appear available on another until the next sync — producing oversells, cancellations, and marketplace penalties. Every new channel or warehouse meant another brittle integration, and during peak sales the batch syncs fell hopelessly behind while the point-to-point web buckled under load.

The problem was not any single system but the wiring between them. eProxim replaced the point-to-point tangle with a middleware layer that made inventory and orders event-driven and gave every channel one authoritative source of truth.

1. Stand up an integration layer that ingests stock, order, and fulfillment events from every channel, ERP, and warehouse through a normalized set of adapters — retiring the point-to-point connections.
2. Maintain a single real-time inventory ledger that every channel reads from and writes to, so a sale anywhere decrements availability everywhere at once.
3. Orchestrate the order lifecycle — capture, allocation, fulfillment, returns — as events, with retries and reconciliation so no message is lost under peak load.