The work behind the systems.
These are working systems, not polished demos. Each case study follows the failure, the decision that mattered, and the honest boundary between what is proven and what is still in progress.
Finding the hidden failure in an AI content pipeline
A content system was passing its own checks and still letting bad facts reach human review. The fix was separating “faithful to the brief” from “actually true.”
02Replacing a $4-per-lead research step with a system that costs nothing
A research pipeline needed specific openers at scale. I replaced an expensive AI audit with deterministic evidence, then found the real risk in the CRM handoff.
03Building an SEO engine that refuses to take credit for its own fixes
An SEO system should not mark a fix complete because the CMS said so. This one checks the public page, preserves before-values, and reports when the site is healthy.
Systems that know where to stop.
Across all three builds, the pattern is the same: automate the repeatable work, make failure visible, and keep a human in the loop wherever judgment or trust is involved. The case studies are written to show the edges too.