Data Quality Intelligence
DQI stands for Data Quality Intelligence. It is a platform for helping organisations understand, improve and govern the data used by AI systems, analytics platforms and automated business processes. DQI is built around three connected capabilities: DQI Assess, DQI Enforce and DQI Integrate. DQI Assess is available now; DQI Enforce and DQI Integrate are in development.
Data Quality Intelligence is a category, not a feature. It treats data quality as a live, governed control problem rather than a one-off cleansing task. It connects three things that are usually managed separately: the state of the data itself, the policies that govern how AI and automation use that data, and the evidence that proves those policies were applied.
Most organisations are now using AI in some form, and most are doing so faster than their governance processes can keep up. Policies live in documents, controls live in spreadsheets, audit lives in PDFs, and the AI itself sits outside all of them. That gap matters because AI amplifies whatever it is given: the data it consumes, the prompts it receives, the outputs it produces.
DQI exists to close that gap. It does not try to replace the AI models, the source systems or the existing analytics stack. It sits above them as a governance, control and evidence layer.
DQI Assess is the assessment engine. It measures data quality, governance maturity and AI readiness across five dimensions, produces a sector benchmark and returns a remediation plan.
DQI Enforce is the in-development policy enforcement layer. It is designed to sit on AI-bound traffic as a governance proxy, evaluate prompts and outputs against organisational policy, and produce a full audit trail.
DQI Integrate is the in-development data preparation and integration layer. It is designed to move data between systems with data quality controls built in and deliver trusted data into AI, analytics and automation workflows.
AI governance is often discussed as if it were a model problem. In practice, the quality of AI outputs is bounded by the quality of the data and the discipline of the controls around the model, not by the model itself. Untrusted data produces untrusted output, and that cannot be fixed at the prompt layer. DQI is built on this premise: govern the data, control the use, evidence the result.