G of Alpha
G of Alpha
Our core AI system pairs InteliAgent — the brain with integrated behavior training — with Alpha‑ML — the information flow controller & signal head. Together they enable real‑time adaptive control across AI, automation, and physical systems, with Alpha‑Adjust extending control to live operation.
Our core pairs InteliAgent — the brain with integrated behavior training — with Alpha‑ML — the information flow controller & signal head. They're supported by Alpha‑Adjust for live modulation.
Signal-driven cognitive system with three core behaviors: knowing, not knowing, and asking.
Learn more →Information tracking and flow control that evaluates signals and adjusts when and how to proceed.
Learn more →InteliAgent includes integrated behavior training for signal-based shaping and adaptive learning.
Learn more →Lightweight, profile-driven API for on-the-fly control stabilization and output modulation.
Learn more →Our control architecture layers atop existing transformers to add targeted controls, advanced modulation, and token-level guidance. We’ve demonstrated portability across TinyLlama 1.1B and Microsoft Phi-4.
Fine-grained behavioral control is essential for safety-critical use cases—from intelligent tutoring and research automation to secure enterprise deployment.
A second pillar of our work is a uniquely formatted, proprietary data layer designed to improve training reliability and trust. This infrastructure is central to a trustworthy AI-data economy and is being expanded in collaboration with partners.
Author, independent philosopher, and AI engineer focused on autonomy, meaning, and human decision-making. Leads the development of adaptive control architectures and a trustworthy AI-data economy aligning contributors, users, and communities.
Seasoned business consultant and advisor across many sectors, focused on strategy, partnerships, and execution. Strengthens organizational growth, stakeholder relationships, and long‑term positioning.
Vice President, WAFUNIF. Provides strategic guidance on responsible technology deployment, policy alignment, and sustainable growth—leveraging decades of science and technology leadership across governments and institutions. LinkedIn
This page is a concise overview. See the research page for full details, features, and methodology.
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This is not a toolkit — it’s an approach. One that values interpretability, responsiveness, and long-term adaptability.
Explore how models or systems behave in controlled conditions. Evaluate edge cases, adaptive limits, or profile-specific reactions in a safe sandbox environment.
Use feedback-sensitive training structures that teach systems how to behave under pressure, not just what to output when prompted.
Embed control modules into any live process to monitor signals and guide output more intelligently. Ideal for feedback loops and changing environments.