Methodology
Most AI projects fail not because the model is wrong, but because nobody thought about who would use it or how. We start with the human problem and work backward to the technical solution.
Philosophy
In 25 years of building systems, the pattern is always the same: technically brilliant solutions that nobody uses. The model accuracy doesn't matter if the workflow is broken. The automation doesn't help if people don't trust it.
We learned this the hard way — building analytics dashboards that gathered dust, recommendation engines that users ignored, and classifiers that created more work than they saved. The technology was sound. The human design wasn't.
We spend more time watching people work than writing code. The real requirements live in workarounds, complaints, and the things nobody thought to mention.
Users need to understand what the AI did and why. Every output should be traceable. Every automation should have an override. Opacity kills adoption.
We design for what happens when the AI is wrong — because it will be wrong. The question is whether that failure is recoverable or catastrophic.
Demos impress. Production teaches. We build systems that survive contact with real users, real edge cases, and real organizational complexity.
Process
Every engagement follows this structure, adapted to scale. The sequence matters — each step builds on the previous one. Skip a step and the system breaks in production.
Georgia Tech MS HCI
Understand users and workflows before writing code. We observe actual work, interview stakeholders, map the real process — not the documented one. Prototype the interface first. If the workflow doesn't make sense on paper, it won't make sense in code.
Software Architecture
Translate understanding into precise specifications. Every input, output, edge case, and failure mode is defined before building begins. The spec becomes the contract between what we promised and what we deliver.
Computer Science Fundamentals
Build modular, testable components. Each function does one thing with predictable inputs and outputs. No side effects, no hidden state, no surprises. This is what makes AI systems debuggable.
Engineering Discipline
Write the test before the code. Every behavior is verified. Every edge case is covered. Our bid automation system has 406 tests. Our zoom tracker has 200+. This isn't overhead — it's how you sleep at night.
Production Experience
Ensure nothing breaks when things change. AI systems evolve — prompts update, models change, data shifts. Regression testing catches drift before users do.
Modern AI Operations
Deploy with AI agents in the loop. Automated quality gates, evaluation pipelines, and continuous improvement. The system gets better in production, not just in development.
Background
This methodology didn't come from a textbook. It emerged from building systems that had to work — in construction offices, virtual classrooms, and mobile apps used by real people.
The foundation is a dual degree in Economics and Computer Science with an AI/ML concentration from UNC Chapel Hill, followed by a Master's in Human-Computer Interaction from Georgia Tech. The economics training shaped how we think about incentives and systems. The CS training gave us the engineering rigor. The HCI training taught us that neither matters without understanding the human in the loop.
Since 2012, the work has been Data Science — building models, pipelines, and intelligent systems across industries. Each project added a lesson. Each failure sharpened the methodology. The 6 steps above aren't theoretical. They're battle-tested.
Most importantly, we've been on both sides. Building systems teaches you what's possible. Using systems teaches you what's needed. That dual perspective is what separates tools that ship from tools that stick.
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