April 10, 2026  ·  5 min read

CAI-007 Safety Cohort Fully Enrolled in Phase I Trial

Our tau aggregation inhibitor targeting early-stage Alzheimer's disease has completed enrollment of the initial safety cohort. Data readout expected Q4 2026.

The practical reality is more nuanced than most introductory material suggests. Context matters significantly—what works for a high-volume, low-margin operation won't transfer directly to a lower-volume, higher-complexity environment. Before applying any general framework, AI engineers and ML teams need to understand which assumptions the framework is making about their environment, and whether those assumptions hold.

The technical layer matters, but it's rarely where implementations fail. The more common failure modes are organizational: unclear ownership of model outputs, no established process for handling edge cases, and the absence of feedback loops that would allow the system to improve over time. Building the model is six months of work. Building the organization around the model is an ongoing commitment that most teams underestimate when they start.

If you're starting from scratch, the most important first step is narrow scope. Pick one area where the problem is most acute and where success or failure will be clearly visible within 90 days. Build proof there before expanding. The temptation to solve the entire problem at once is understandable but usually counterproductive—broader scope means slower feedback, more dependencies, and more opportunities for the initiative to lose momentum before it demonstrates value. Start narrow, prove the model, then scale what works.