About
Enablement Engineering builds production AI systems for workflows generic tools cannot handle.
Document transformation. Accessibility-aware content pipelines. Review processes that need evidence.
Led by Dylan Isaac, the practice combines accessibility, agentic systems, and production engineering.
What we’ve built
Our work includes Equalify Reflow with UIC Digital Accessibility Services: a production AI pipeline that transforms inaccessible academic PDFs into semantic, accessibility-first web content. This work was presented at CSUN 2026.
Previously at Deque, Dylan built axe Assistant and developed production approaches to AI-generated accessibility content.
That experience matters because accessibility-aware AI systems fail in specific ways: semantics get flattened, evidence disappears, mistakes hide inside confident output, and review teams need more than a good demo. We build with those failure modes in view from the start.
View Enablement Engineering on GitHub (opens in a new tab) or view Dylan on GitHub (opens in a new tab).
Working principles
Ladders, not walls.
Systems should increase the team's capability, not create a black box only the vendor can operate.
Mirrors, not slot machines.
AI should reflect the team's standards, constraints, and review process, not optimize for novelty or plausible output.
Visible enough to challenge.
Reasoning, tool use, review points, and failure states should be visible enough for humans to inspect and correct.
How we engage
We usually start with paid discovery: a short working engagement against real content, a real workflow, and a real artifact. That gives both sides enough evidence to scope a build responsibly.
The goal is not to make clients dependent on us. The goal is to leave behind systems, procedures, evaluation criteria, and documentation that teams can understand, inspect, and improve.