Case Studies on Serenity Software

The $5M rewrite, delivered for $500K

Mon, 01 Jan 0001 00:00:00 +0000

The situation. An established platform that everyone was afraid to touch. Releases were slow and risky, delivery had ground to a crawl, and the plan on the table was a full rewrite: $5 million and years of parallel work while the old system kept running.

What I found. The $5 million wasn’t the price of what the business needed. It was the price of rebuilding everything, including features nobody had opened in years. When we listed what the business actually used and made money from, it was a much smaller system than anyone assumed.

From MVP to production-grade in 4 months, not 2 years

Mon, 01 Jan 0001 00:00:00 +0000

The situation. A funded startup with a working MVP and paying customers. The product was breaking under real usage, and the plan to fix it properly was a two-year rebuild. They didn’t have two years. Nobody at that stage does.

What I found. Most of the plan was building for scale they didn’t have yet. New infrastructure, new abstractions, rewrites of things that already worked. Meanwhile the actual complaints came from a handful of places: a few unstable features, deploys everyone was scared of, and bugs that customers found before the team did.

From WordPress to a real platform: 3x the business in 18 months

Mon, 01 Jan 0001 00:00:00 +0000

The situation. An education company running on WordPress. The site had carried them from idea to a real business, and now it was the thing holding them back. Errors were constant, new features took months, and bigger customers were asking for a white-label product the site had no way to offer.

What I found. WordPress wasn’t the villain. The product had simply outgrown it: years of plugins and custom code stacked on top of each other, where every change broke something else and every error was hard to trace. The business they’d become needed an architecture the original site was never going to grow into.

The legacy rebuild that doubled revenue in 2 years

Mon, 01 Jan 0001 00:00:00 +0000

The situation. A mature membership business on a website that had been cobbled together over many years. Processes held together with duct tape, an architecture that failed in a new way every month, and pages that took multiple seconds to load. The team spent its energy keeping the thing alive instead of making it better.

What I found. No single disaster, just years of shortcuts that had compounded. Every part of the system worked slightly differently, nothing trusted anything else, and the slowness and the breakage came from the same root: nobody had ever unified how things got built and shipped.

The two-person team that outran bigger competitors

Mon, 01 Jan 0001 00:00:00 +0000

The situation. Two founders with a good product and a backlog they were never going to finish. Every week went into grinding through development work, and the backlog got longer anyway. Competitors with far bigger teams were shipping faster than they could.

What I found. They didn’t need more people. A big share of their development process was repetitive work that a machine should have been doing, and the two of them were spending founder hours on things that didn’t need a founder.

An IoT refactor done in 2 months, not 2 years

Mon, 01 Jan 0001 00:00:00 +0000

The situation. An IoT platform on a microservices architecture that had grown hard to live with. Performance problems, fragile services, and data nobody fully trusted: flaky sensors, third-party feeds that sent odd values, and code that was risky to change. The internal estimate for fixing it properly was two years.

What I found. The architecture didn’t need to be thrown out. It needed fewer moving parts, clearer boundaries, and a serious stance on data quality. Most of the firefighting traced back to bad data getting deep into the system before anyone noticed it was bad.

Terabytes a day through an architecture that just works

Mon, 01 Jan 0001 00:00:00 +0000

The situation. A client that needed to ingest and process serious volumes of streaming data, terabytes every day, and needed an architecture that could handle it reliably without an army to keep it running.

What I found. This is one of the few problems where microservices genuinely earn their keep. Most teams reach for them for the wrong reasons and pay for it in operational pain. Here the shape of the problem actually matched the shape of the solution: independent stages, very different scaling needs, and clear boundaries between them.