The Psychology Network Pty Ltd conducts advanced research and development in AI safety, explainability, and regulatory-grade alignment. Our work focuses on enabling large language models to operate transparently, predictably, and in accordance with explicit ethical, legal, and professional standards—particularly in safety-critical domains such as healthcare, mental health, and regulated professional services.
Our core technology, JoBot™, is built on the Localist LLM framework, a hybrid AI architecture that combines symbolic rule systems with large language models while introducing structured, controllable locality into model behaviour. Unlike purely black-box approaches, Localist LLMs are designed to relate model outputs to identifiable architectural components, enabling verification, traceability, and auditability across different levels of model access.
At the heart of JoBot™ is the Localist LLM Alignment Layer, a model-agnostic governance and safety layer that operates across a spectrum of deployment scenarios—from API-only foundation models to fully integrated, self-hosted or proprietary LLM stacks. Depending on the level of access to model internals, the alignment layer provides progressively stronger guarantees of interpretability, behavioural control, and domain separation, without requiring retraining of the underlying foundation model.
JoBot™ enables large language models to function under explicit, runtime-configurable rule frameworks. Symbolic rules can be injected, updated, and reloaded dynamically, allowing organisations to incorporate evolving regulatory requirements, professional codes of conduct, and internal governance policies directly into AI behaviour. At deeper integration levels, localist mechanisms such as structured attention alignment and progressive localisation provide measurable, auditable insight into how and why a model reaches specific outputs.
This approach transforms AI systems from static, opaque tools into living architectures for responsible deployment. By unifying symbolic reasoning with localist control of neural representations, the Psychology Network Pty Ltd delivers AI systems that are safe by design, aligned with emerging regulations such as the EU AI Act, and suitable for certification, audit, and long-term use in high-risk environments.
Research
Our research explores how symbolic rules can be combined with deep-learning systems to create transparent and trustworthy models. We study the mathematical foundations of rule-guided large language models and develop proofs that show how constraints can influence model behaviour with predictable outcomes. This work provides a theoretical foundation for aligning complex models with human-defined standards.
In applied projects, we evaluate JoBot's™ reasoning processes, analysing how rule-guided architectures improve consistency and explainability in daily workflows. The team also investigates how “localist representations”, compact, interpretable neural network units, can enhance mechanistic interpretability while maintaining strong generalisation performance. Read more about our current research.
Patents
The company’s patent portfolio reflects decades of work on AI safety, explainability, and alignment. This includes Localist LLMs, a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining.
Other inventions cover methods for dynamically injecting and updating rules within neural systems, an approach sometimes referred to as “hot reloading.” This enables continuous compliance with evolving professional or regulatory standards without retraining the underlying model.
Other patents focus on extracting symbolic rules from deep neural networks to produce human-readable rationales and visual explanations. These techniques provide the link between the opaque internal computations of large models and the requirements of legal, or organisational accountability.
Collectively, these patents define a framework for building AI systems that are traceable, auditable, and adaptable: properties that are essential in safety-critical domains. View our full list of patents.
Software
Psychology Network develops modular software components that operationalise JoBot's™ safety principles. These include rule engines for real-time constraint enforcement, Python toolkits for alignment testing, and integration interfaces for existing data and workflow systems. The components are designed for clarity, verifiability, and ease of integration.
The software library is continually evolving through research collaborations and internal testing. Upcoming versions will include expanded documentation, visual inspection tools, and multi-media explanation generation. Explore our software offerings.
Health Focus
Healthcare presents unique challenges for artificial intelligence: data sensitivity, regulatory oversight, and the need for professional accountability. Our work addresses these challenges by embedding ethical and procedural safeguards directly into AI behaviour. JoBot's™ rule-based architecture ensures that clinical reasoning remains transparent and reviewable at every stage.
We collaborate with clinicians and researchers to model standard psychological and medical workflows—such as intake interviews, structured assessments, and report generation—within rule-compliant AI frameworks. The resulting systems can explain their recommendations, reference their governing rules, and provide clear audit trails for human supervisors.
By aligning computational intelligence with established clinical practice, the Psychology Network Pty Ltd aims to advance safe and trustworthy AI in healthcare. Our mission is to ensure that AI complements professional judgement rather than replaces it, maintaining both human oversight and technological innovation.