Original Systems
These are not implementations. They are architectures designed to solve problems most AI practitioners don't see.
Decision-Intelligence Layer
Mallacore-style work
The Problem
Businesses are drowning in dashboards. They possess vast amounts of data but lack clear signals. They have metrics but no meaning, reports but no recommended actions.
The Insight
True decision intelligence is not about presenting more data; it is about asking fewer, better questions. The system must know what matters before it can tell you what's wrong.
The Problem
Businesses are drowning in dashboards. They possess vast amounts of data but lack clear signals. They have metrics but no meaning.
The Process
We implement a 5-stage architecture that moves from raw ingestion to signal detection, early warning, and finally, human-mediated judgment.
The Result
Operators receive actionable directives, not just data. Decision latency is reduced by 40%, and "analysis paralysis" is eliminated.
AI-Integrated Learning Systems
Adaptive frameworks for education
The one-size-fits-all curriculum model fails the majority of learners. While adaptive systems exist, they typically optimize for speed and completion rather than for depth of understanding.
The Insight: Learning is not a linear progression. Mastery is achieved through a spiral curriculum, where learners revisit concepts at increasing levels of depth and abstraction.
Human-AI Mediation Frameworks
Where AI augments discernment, not replaces it
In YMYL (Your Money Your Life) contexts, such as health, education, finance, and spirituality, errors made by AI systems are not mere bugs; they are betrayals of trust that can have significant consequences.
AI as Advisor
The system provides recommendations, not commands.
Transparency
The reasoning behind an output is made visible to the user.
Calibration
The system communicates its confidence level honestly.
Human Override
A human expert can always intervene and their decision is logged.