Machine Intelligence Lab
The Machine Intelligence Lab is where we build the future of responsible AI. It’s our internal research and development studio, home to the proprietary tools and verification methods that power our consulting services, standards, and certifications. From SPRI™ (Socratic Prompt Response Instruction) to behavioural maturity scoring, we develop systems that make AI more transparent, traceable, and safe to deploy. We don’t just use AI—we design the frameworks that keep it aligned with human values.
SAGE™
The Problem: Most AI systems can generate fluent language, but they can’t explain their reasoning, identify their limits, or defend their outputs.
The Second Problem: Verification layers in large models are often post-hoc, brittle, or opaque, making accountability difficult to scale.
The Third Way: TWC’s Socratic Architecture Governance Engine (SAGE™) framework, which includes SPRI™ (shown to reduce LLM hallucination rates to zero in testing), is a Socratic reasoning framework that layers structured, transparent prompting over generative systems—enabling verified outputs, traceable logic, and human review. SAGE™ tools, which are private, secure, and encrypted, power everything we do. This means that every piece of data and advice we offer is certified as accurate and verifiable.
Behavioural Analysis
The Problem: Traditional standards, certification systems, and assessment frameworks often measure intent, not actual behaviour. This produces a gap between policy and practice.
The Second Problem: Traditional maturity models lack granularity and fail to account for cultural, cognitive, or system-specific variations.
The Third Way: We’ve developed behavioural scoring systems that assess how AI, as well as various other types of assessments, including risk modelling and program reviews, are conducted, applied, and understood across both people and machines. Our maturity maps are used across TRUST-AI™ and internal client assessments.
Research & Experimentation
The Problem: Responsible AI remains an emerging field, often driven by theory rather than real-world feedback.
The Second Problem: Many models are evaluated in isolation, disconnected from the systems and stakeholders they affect.
The Third Way: Our lab runs field experiments, scenario tests, and model evaluations grounded in human context. We focus on applied insight, not academic publishing, so our findings translate directly into better systems. We publish our findings directly on our website and in other publications.
