How our AI research capability works

Large language models (LLMs) can write, summarise, and converse with remarkable fluency, but they also highlight a growing problem: trust. These systems can produce convincing answers without being grounded in verifiable facts. Their internal reasoning is hidden, sources undisclosed, and outputs often hallucinated. The response to this challenge has been a call for greater transparency: to open up the black box, share their training data, and explain how complex models reach their conclusions. But such explanations are often partial, interpretive, or after the fact. They may clarify model behaviour, yet they cannot make opaque systems truly verifiable.

Transparent AI, as developed by Glass.AI, takes a fundamentally different approach. Where most AI transparency efforts focus on explaining model outputs, Transparent AI is designed to make those explanations unnecessary. Instead of relying on statistical models trained on historical data, Glass.AI builds its intelligence from traceable, real-world evidence. Every data point is sourced, validated, and documented. Each connection between entities can be traced back to the underlying evidence that supports it. The result is a system that is auditable by design, not one that needs to be reverse-engineered to justify its decisions. Transparent AI doesn’t aim to make black boxes visible; it avoids building them in the first place.

Evidence-based AI

Generative AI and LLMs are model-driven: they learn from examples and generate outputs based on probabilistic predictions. This can create surface-level coherence without underlying truth. Transparent AI is evidence-led: it builds knowledge directly from verifiable sources rather than inferred patterns. Building on this principle, Glass.AI enables large-scale discovery and analysis across trusted open and public sources. Our systems automatically extract, structure, and link evidence to create verified intelligence that can be traced and audited at every stage, not inferred from hidden training data. Where generative models estimate what might be true, Transparent AI shows what is true and where it came from.

Crucially, Transparent AI does not rely on any single source of information. Each insight is cross-validated across multiple independent sources—from company websites and regulatory filings to official registers, media coverage, and academic publications. By comparing signals across these diverse datasets, inconsistencies and anomalies can be identified, corroborated, or flagged for review. This process transforms raw data into substantiated evidence, giving users confidence that every output has been independently verified from multiple perspectives. Cross-validation ensures that Glass.AI’s intelligence is not only transparent in origin, but resilient against bias, misinformation, and single-source dependency. It turns the open web into a network of verifiable references rather than a field of untested claims.