Per-claim verification of generative AI output.
T.R.U.S.T verifies AI-generated content against its source. Every factual claim is traced, scored, and flagged when it cannot be substantiated. Built for environments where AI outputs become regulatory record.
Rather than producing a single confidence score, T.R.U.S.T evaluates model output across five independently-trained verification dimensions. Each dimension specialises in a distinct category of reasoning failure, allowing reviewers to focus precisely on the aspect of output requiring attention.
Detects internal inconsistency, invalid inference steps, and reasoning that does not follow from stated premises. Particularly relevant to multi-step analytical workflows.
Detects omitted or incorrectly applied qualifiers and modifiers — for example, generalisations from a sample to a population without supporting evidence.
Detects unit-of-measure errors, currency mismatches, and inconsistencies in dimensional analysis. Critical for financial and scientific applications.
Detects misattribution and unsupported claims. Verifies that conclusions drawn from cited sources are consistent with the underlying source content.
Detects calculation errors, incorrect aggregation, and statistical misapplication. Particularly relevant to quantitative analysis and numerical reporting.
T.R.U.S.T supports three deployment models to accommodate the operational and regulatory constraints of customer environments. Selection between them is typically determined by data residency, network architecture, and audit requirements.
Managed REST API hosted on Aigos infrastructure. Lowest operational burden, suitable for non-sensitive workloads and proof-of-value engagements.
Container deployment within customer infrastructure (AWS, Azure, GCP, or private cloud). Customer retains full control of data plane; updates delivered through signed registry.
Air-gapped binary distribution for classified, sovereign, and otherwise restricted environments. No outbound network connectivity required at any stage of operation.
T.R.U.S.T is deployed in production at organisations where the consequences of unverified AI output are operationally and legally significant. Representative applications are described below; full case studies are available under non-disclosure to qualified prospects.
Verification of AI-generated equity research, credit memoranda, and regulatory filings prior to analyst review.
Verification of AI-assisted clinical documentation, prior-authorisation correspondence, and patient communication drafts.
Verification of AI-assisted policy analysis, briefing materials, and constituent correspondence prior to publication.
Verification of AI-generated case research, deposition summaries, and contract analysis prior to attorney review.
The T.R.U.S.T browser extension presents verification results inline alongside generative model output, eliminating the context-switching that arises from evaluating AI-generated content in a separate review interface. The extension supports ChatGPT, Claude, Gemini, and Perplexity, and integrates with internal LLM deployments via configurable endpoints.
"In our analysis of the 2023 fiscal year, revenue grew by 23% over the prior period, driven primarily by enterprise contract expansion."
"This represents the strongest growth in the company's history."
Initial engagements typically include a verification accuracy benchmark against representative output from your existing AI workflow, followed by deployment planning aligned to your regulatory and audit requirements.