George
AbrahamPh.D.
I'm a design leader with a Ph.D. in Human-Centered Design and 16 years designing platform products for developers and analysts. The work starts in user research and ends in production code, and I've done both ends.
Each platform I built solved the bottleneck the previous one exposed. Modeling real user workflows raised the question of whether they actually worked, so the next step was validating decisions before development. Validated designs still got rebuilt by hand, which led to generating production code from them. Once AI entered that pipeline, the job became designing the constraints that make its output trustworthy.
Get in touchThe gap between design and engineering isn't a communication problem. It's a systems problem. I build the systems that close it.
Model the Behavior
Designers were drawing screens while engineers built systems, so the states, transitions, and edge cases between them weren't in anyone's deliverable. They got discovered in code, usually too late to fix cheaply.
Prove It Works
Teams were shipping on gut feel and stakeholder sign-off, because real usability testing was too slow and expensive to run regularly. Most designs went to engineering without anyone knowing if they'd actually work.
Ship Without Waste
Even after a design was validated, engineers rebuilt it from scratch using the spec as a reference. Every handoff was a retranslation, which meant layout fidelity eroded a little more each time. When AI generation entered the platform, the problem changed, since a non-deterministic model now had to produce output an enterprise design tool could trust.
Prototype the Complex
Enterprise BI teams needed a semantic modeling layer with no existing UX pattern to reference. The domain was technically dense, the audience unforgiving, and direction had to be validated before any production code was written.