Case Studies

Full Platform Rewrite - Delivered Incrementally, Zero Downtime

Enterprise SaaS • Application Modernization • Cloud Infrastructure • C# • .NET

The Problem

A B2B software company had a core platform that was failing under its own weight. The application was running at full server utilization with significant cloud infrastructure cost overruns. Performance was degrading as the user base grew, and a key client relationship was at risk due to stalled feature development under the previous technical leadership. The codebase had accumulated years of shortcuts and the architecture couldn't support the product roadmap.

The business couldn't afford to stop and rebuild. The platform was in active use - customers depended on it daily. A traditional rewrite would have meant months of parallel development with a risky cutover at the end.

The Approach

We chose a different path: replace the platform one page at a time. From the outside, users saw a series of UI improvements rolling out month by month. In reality, each page was being rebuilt on an entirely new architecture and deployed to replace its predecessor. No big-bang migration. No cutover weekend. No downtime.

Simultaneously, we re-architected the hosting infrastructure and right-sized the cloud footprint to match actual workload requirements rather than over-provisioned legacy defaults.

The Outcome

The entire platform was replaced without a single day of service interruption. Infrastructure costs were reduced by approximately 80% while server utilization dropped from full capacity to under 25%, delivering more than 4x the headroom for growth. The active user base doubled during the rewrite period. The at-risk client relationship was recovered within two weeks of the engagement starting.

Technologies

C#, .NET, cloud infrastructure, API redesign

SCADA Modernization & AI-Driven Equipment Monitoring

Energy • SCADA • Timeseries • Python • AWS

The Problem

A major energy services company operated a SCADA platform monitoring thousands of well sites across North America. The system ingested millions of data points per day from submersible pumps, artificial lift equipment, and wellhead sensors, feeding a PostgreSQL timeseries database that had grown past 10 billion rows.

The platform was functional but strained. Visualization tools lagged behind the data volume. Alerting was reactive - field teams learned about equipment failures after the damage was done. The architecture hadn't been designed for the scale it had reached, and the engineers who originally built it had moved on.

The Approach

We led multiple engineering teams through a modernization that touched every layer: data ingestion, storage, real-time processing, and end-user visualization. The PostgreSQL timeseries layer was re-architected for sustained write throughput and fast analytical queries at scale. A near-realtime event processing pipeline was built on AWS Lambda, cutting response time to field conditions from hours to under a minute.

The most significant addition was a machine learning system to monitor Electronic Submersible Pump health, predicting equipment failures before they happened and shifting field maintenance from reactive to predictive.

The Outcome

The modernized platform handled SCADA data at scale with sub-minute latency. Predictive monitoring reduced unplanned equipment failures. Field operations teams gained real-time visibility into conditions that had previously required manual review. The architecture was designed to keep scaling - built for the next 10 billion rows, not just the ones already in the database.

Technologies

Python, Django, React, Angular, PostgreSQL (timeseries), AWS Lambda, Docker

Real-Time Field Data Platform with Industry Integrations

Energy • Oil & Gas • Real-Time Data • Integrations • Embedded Systems

The Problem

Oil and gas operators needed a way to collect, analyze, and act on field data in real time - production metrics, well site conditions, and equipment status - across a fragmented ecosystem of industry-specific software platforms. Each operator ran a different combination of production accounting, measurement, and decline analysis tools, and none of them talked to each other. Field data was siloed, delayed, and manually reconciled.

The Approach

We built a web and mobile platform from the ground up for real-time collection and analysis of operational metrics across the upstream oil and gas lifecycle. The platform included direct, production-grade integrations with industry systems including production accounting, flow measurement, decline analysis, and field monitoring platforms.

Beyond software, the engagement required coordination with an external embedded device vendor to develop custom field hardware for data collection at well sites - bridging the gap between legacy field equipment and a modern data platform.

The result was a system spanning more than half a dozen interconnected applications - web, mobile, integration, and data pipeline - built and maintained by a team we hired, trained, and led.

The Outcome

Operators gained a single, real-time view of field operations that had previously been scattered across disconnected systems and manual reports. Data that once took days to reconcile was available in minutes. The platform's integration layer became its primary competitive advantage - connecting to the systems operators already ran, rather than asking them to replace everything at once.

Technologies

PostgreSQL, Redis, Node.js, Java, AWS, Docker, Nginx, C, Objective-C