We are based in Colorado, building the tools that keep packaged software implementations grounded in reality as AI takes on more of the work.
As AI agents got fast and accurate at writing code, the bottleneck in large packaged software implementations didn't disappear — it moved upstream. The constraint isn't generating code anymore. It's capturing what the code should actually do, accurately, from the people who know.
DreamCatcher exists to close that gap. Four tools work together toward one goal: give AI agents — and the humans reviewing their work — requirements that are grounded in what the implementation actually is, not what someone remembers or assumes it to be.
DreamCatcher started as a Java application built to capture packaged software implementation requirements by hand. It has since grown into a full web-based, AI-assisted platform — the same underlying goal, considerably more capable tools.
We don't trust model confidence as a substitute for a person saying yes. Every significant AI output in the DreamCatcher pipeline — a solution recommendation, a design document — passes through an explicit approval gate. That's not a limitation we're working around; it's the design principle the whole platform is built on.