Engineers who shipped systems and documented what broke.
Groundwork AI was built by engineers with production ML systems behind them — not consultants who studied AI from the outside. The failures taught us more than the wins.
Senior engineers on every engagement.
We keep the team small deliberately. Every project has the people who scoped it also building it — no senior pitch, no junior delivery.
Our background is in data pipelines, model deployment, and the integration work that keeps AI systems running in production long after launch day.
Most AI projects fail at the question, not the algorithm.
Before we write a line of code, we spend weeks interrogating the problem statement. Wrong data and wrong questions account for more failed systems than any model choice.
We document what we learn — including the dead ends. That record is part of what we deliver, because the next team inheriting this system needs to know what was already tried.
An AI system is 90% data infrastructure and integration work. We are obsessive about that layer — because that is where production systems live or die.