I spent years watching good operators run their businesses on duct tape and spreadsheets. In fact, for much of my career, I've been that operator. Not because I didn't know better, but because nobody had built anything better.
Every industry has this. A workflow held together by a shared Google Sheet, three logins, and someone's memory of how it's always been done. Ask the owner why, and you get the same answer every time: "that's just how it works here," or, "it's the best workaround we have."
But why all the workarounds? Because the tech industry has been bad at building accessible tools with any useful degree of industry or entity-level specificity.
This is a problem worth solving. That's why we started Algebra.
The problem nobody thought was worth solving
An Algebra Problem is a persistent operational challenge that an underserved industry has accepted as the cost of doing business. Not because it's unsolvable. Because the software market moved on to bigger markets and left these industries to figure it out themselves.
I've sat across the table from operators who apologized for their own systems before I even asked a question. Three tools that don't talk to each other. A spreadsheet that only one person understands. A process that breaks the moment that person goes on vacation. They weren't looking for something flashy. They were looking for someone to take the problem seriously.
That's the gap Algebra exists to close.
Domain first. Code second.
We don't start with a generic software pattern and go looking for a vertical to apply it to. We start with the operator. We watch how the work actually gets done, where it breaks, and what it costs them when it does. The code comes after we understand the problem completely, not before.
This matters more than it sounds like it should. Most software built for underserved industries fails because it was designed by people who researched the problem instead of living inside it. It looks right in a demo. It falls apart on the second day when the real workflow shows up with all its exceptions and edge cases.
AI-native. Engineer-built.
The tools available to build software have changed faster in the last two years than in the previous twenty. Custom software that used to take a year can now take weeks, sometimes days. That speed is real, and we use it.
But speed isn't the hard part anymore. The hard part is everything that comes after the first working version: the architecture that holds up under real use, the cost controls that keep an AI bill from outgrowing revenue, the security that keeps one weak door from exposing every customer record. In this new age, I remind people all the time (sometimes after they've already gotten themselves in trouble): A working prompt is not a shipped product.
So we build AI-native, and we build engineer-built. AI gives us speed. Senior engineering gives us discipline. We use every AI tool available, and we ship only what's been reviewed, hardened, and understood by someone who put their name on it.
What comes next
Algebra is Daniel LeMay and me, building software for industries the market forgot to serve well. Not because it's a good tagline. Because we've both spent our careers on the operator's side of that gap, and we know what it costs to be left there.
Over the coming weeks, Daniel and I will be trading off this space to talk through what we're building and why. He'll cover the technical decisions. I'll cover the operational ones. Different vantage points on the same problem.
That's an Algebra Problem. Solving.