Cognitive infrastructure that grows with every interaction, not monolithic models that start from zero. Our patented systems combine neural memory, adaptive routing, and continuous self-learning to create AI that compounds intelligence over time.
Patent filed, 3 in submission
Active research projects
Founded in London, UK
IDRAAK AI was founded on a simple observation: current AI systems are stateless. Every conversation starts from zero. Every decision ignores history. Every context window eventually overflows.
We build cognitive architectures that give AI systems persistent memory, self-learning behaviour, and organic growth. Every decision is traceable, every reasoning chain has lineage, and the system evolves with use — not through retraining. Our architectures are designed for domains where getting it wrong has real consequences — healthcare, energy, financial services, regulatory compliance.
The name IDRAAK (إدراك) means perception and cognition in Arabic — the capacity to truly understand, not just process.
Every AI output traces back to its source. Full chain-of-thought lineage, exact document references, zero hallucination tolerance in safety-critical domains.
Systems that learn from their own operational history. Parametric routing that improves with use, not just static rules that decay with context.
Memory architectures that compound knowledge across sessions, projects, and years. Context that never overflows because it's structured, not stuffed.
Sensitive data stays on-premise. Our systems run on local hardware, with no dependency on cloud APIs for core reasoning. Privacy by architecture, not by policy.
Our research programme spans neural memory systems, adaptive model routing, self-organising knowledge representations, and activation-based retrieval. Each project addresses a fundamental limitation of current AI systems.
A novel architecture for intelligent decision routing in AI systems, addressing fundamental limitations in how current systems maintain coherence across extended interactions.
A biologically-inspired approach to knowledge organisation that enables emergent structure formation, drawing on principles from computational neuroscience and competitive learning.
Techniques for efficiently adapting general-purpose AI models to specialised domains without requiring full retraining, reducing both cost and deployment complexity.
An approach to enabling controlled reasoning across isolated knowledge domains, allowing specialised systems to collaborate without compromising the integrity of individual domains.
Our technology powers real systems across AI governance, intelligent automation, and research — each built on the same cognitive infrastructure layer.
Additional projects in stealth across energy, decarbonisation, and domain-specific AI.
AI architect with a decade of experience spanning machine learning, data engineering, and systems architecture. Research interests in cognitive AI systems, neural memory architectures, and the intersection of biological cognition with computational intelligence.
We're open to research collaborations, licensing discussions, and partnerships in safety-critical AI deployment.
obaid@idraakai.co.uk