How It Works · AI Quality Assessment
Our AI systems are subject to a structured, recurring quality assessment. The methodology is custom-designed for the specific demands of regulatory intelligence systems and is conducted on a regular basis to track system performance and support continuous improvement.
What We Test
The assessment framework was developed specifically for the properties that matter in regulatory applicability classification. Each dimension tests a distinct failure mode; a material deficiency in any one of them would compromise the integrity of the system's output.
Do repeated evaluations of identical regulatory documents yield statistically invariant applicability classifications across independent runs?
Are applicability determinations invariant under systematic removal of entity-specific contextual identifiers from the input?
Do applicability classifications converge across architecturally distinct AI and language model implementations evaluated on the same document corpus?
Validation Framework
Our system is not classified as high-risk under the EU AI Act. Nonetheless, we voluntarily orient our methodology around the accuracy and robustness principles of Article 15 and the human oversight framework of Article 14, treating them as a best-practice benchmark.
EU AI Act Art. 14 & 15 — voluntary best-practice benchmarkWhy It Matters
Repeating the assessment at regular intervals serves two purposes: it generates an ongoing record of system performance, and it drives the development cycle by making regression and improvement measurable. The methodology is not a one-time certification exercise but a living part of how we operate the system.
Each assessment round produces a structured report with method descriptions, test corpora, and quantitative results. This report can be shared with internal audit, compliance officers, or external auditors as part of a broader AI governance programme.
Recurring measurement
The assessment is conducted periodically, not as a one-time gate. Results across cycles are comparable, making performance trends observable over time.
Development input
Quantitative scores from each cycle feed directly into the engineering and model selection process, linking observed quality gaps to concrete improvement actions.
Voluntary best-practice alignment
Although our system is not high-risk under the EU AI Act, the framework draws on the accuracy and human oversight principles of Articles 14 and 15 as a voluntary quality benchmark.
Available on Request
The complete report, including quantitative results across all three dimensions and methodology documentation, is available to qualified prospects on request. If your firm requires documented AI quality evidence as part of a procurement, due diligence, or governance process, please get in touch.