Most software projects focus on producing outputs.
Most AI projects focus on producing predictions.
But real organizations don't operate on outputs or predictions alone.
They operate on decisions.
A decision has consequences.
A decision creates risk.
A decision consumes resources.
A decision changes the future state of a system.
Over the last few months, I've been studying and building systems around a simple question:
How can we make decisions more explainable, auditable, and repeatable?
This led me toward concepts such as:
event-driven architectures
decision logging
risk evaluation pipelines
audit trails
feedback loops
operational intelligence systems
Instead of asking:
"Can we predict what will happen?"
I'm becoming more interested in asking:
"Can we explain why a decision was made?"
and
"Can we reproduce that decision six months later?"
Current areas I'm exploring:
Financial decision systems
Risk infrastructure
Event-driven architectures
Blockchain compliance workflows
Operational intelligence platforms
One of the projects I'm currently building is an Event-Driven Decision Logging System (EDDL), designed to explore how organizations can record, audit, and replay critical decisions over time.
Still learning.
Still building.
Still refining my understanding of how complex systems operate under uncertainty.
Looking forward to sharing the journey here.
![[System Design] GraphHopper Distance Matrix: Self-Host OSRM vs Haversine for Route Optimization](https://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fyoo5j6x1kubw2zbx6ayz.png)
