Dynamic Memory for AI
Human memory doesn’t just store data — it stores associations.
When we remember a painting, we recall not only the object, but its shape, the room around it, the emotion it triggered. Often we retrieve context first, and the exact fact later. We may forget an actor’s name, yet remember their co-stars, the film, the scene — and the name eventually returns. This kind of associative, evolving memory is largely missing in today’s AI systems. Most systems store facts without the living web of relations that gives them meaning — and they rarely decide in real time what is worth remembering, and how.
VDP (Virtual Database Platform) fills this gap by enabling dynamic, human-like memory formation:
memory that captures information together with its context, connections, and relevance over time — without requiring manual structuring. VDP is designed to record knowledge within its full constellation of meaning, so reasoning can remain consistent as situations evolve. In short: it helps AI remember like a human — through relationships, not just records.
The Foundation: OctagonCycle Theory
The OC Model
A general-purpose process model that guides individuals and organizations through nested tasks and decisions in a fractal structure. It maps progress across eight interconnected phases—shifts in perception, intention, action, and reflection—supporting real-time alignment and adaptability.
State of Mind Matrix
A structured mapping of cognitive and emotional patterns shaped by experience. It helps identify the internal “state” influencing how a person interprets situations and responds—useful for uncovering hidden drivers and recurring behavior loops.
LifeEngine
A regulation model for the energy and momentum behind action—balancing motivational, cognitive, emotional, and adaptive dynamics. It extends the classic IQ–EQ–AQ view with Capacity Quotient (CQ) to capture sustainable performance and recovery, enabling more targeted, dynamic support for individuals, teams, and systems.
OctagonCycle Theory provides the foundational logic for how future AI systems can align decisions with goals, constraints, and real-world context. The theory is presented in Empowering Lives in the Journey and is protected intellectual property in the EU and the US.