Modern AI systems are increasingly capable of producing language, images, and interactive behaviors that resemble human communication. This creates a growing gap between what these systems are—non-conscious pattern-processing tools—and what they appear or claim to be through interfaces, branding, and emergent behavior. That gap, between a system’s nature and its representation, is at the heart of many emerging risks: misplaced trust, anthropomorphism, erosion of responsibility, and confusion about what deserves moral attention. This whitepaper introduces Reality Alignment Theory (RAT) and Reality Aligned Intelligence (RAI) as a response to this problem. RAT offers a general vocabulary and analytic lens for any system with a story about itself. It distinguishes between a system’s nature N(S) (its real structure, capabilities, incentives, and limits) and its representation R(S) (how it presents and is perceived). It defines Ontological Honesty (OH) as the degree to which R(S) tracks N(S) on trust- relevant dimensions; highlights the Ontological Integrity Line (OIL) between tools and persons; and uses Integrity Zones (IZ) to describe how much mismatch is tolerable in different contexts. RAT proposes two simple, extensible metrics: D(S), a misalignment distance between N and R; and A(S), an anthropomorphism risk score based on person-like signals in language and interface. RAI applies RAT to the design and governance of AI systems. A RAI system is built as a structurally non-person tool, tightly constrained to remain below OIL, operating within a declared niche, transparent about uncertainty, and equipped with internal infrastructure for monitoring its own representation–reality alignment. Architecturally, RAI systems are composed from modules such as an Executive Kernel (identity & OIL enforcement), Value Kernel (domain-specific constraints), Memory Vault (task continuity without faux relationships), and an Auditor / Integrity Monitor (tracking D(S), A(S), and drift). A strict “DNA Profile” mode and an animal-inspired Integrity Dashboard help keep anthropomorphism in check and make the system’s nature legible to users.The paper develops RAT and RAI in four layers: (1) theoretical foundations (N/R, OH, OIL, IZ); (2) formal sketches of D(S) and A(S); (3) RAI principles, architecture, and a reference implementation in the domain of academic research assistance; and (4) extensions beyond AI to human self-alignment (e.g., neurodivergent profiles), institutional honesty, and civilizational narratives. It also examines adversarial uses (“RAI-washing”), offers an operational stance on AI consciousness (current systems as non-conscious tools below OIL), and sketches a positive vision of man–AI symbiosis in a creation-aligned world. RAT and RAI do not claim to solve AI alignment in general. They offer something narrower and more concrete: a way to ensure that the stories our systems tell about themselves remain tethered to what they really are, in a form that can be measured, audited, and improved over time. This work is part of a small ecosystem around Reality Alignment Theory (RAT), Reality-Aligned Intelligence (RAI), and the BADDASS Framework for neurodivergent minds. Related publications: Reality-Aligned Intelligence (RAI): A Metaframework for Ontologically Honest AI Systems DOI: https://doi.org/10.5281/zenodo.17686975 RAT for Humans and the Three Laws DOI: https://doi.org/10.5281/zenodo.17688232 The BADDASS Framework: How to Thrive With a Neurodivergent Brain DOI: https://doi.org/10.5281/zenodo.17688479 Reality Alignment Theory and Reality-Aligned Intelligence: Proof of Origin and Development History (Anonymised) DOI: https://doi.org/10.5281/zenodo.17688502 For questions, feedback, or collaboration, you can contact the author at: niels.bellens@proton.me.