Data Quality Intelligence
Short, factual answers to the questions DQI gets asked most often. For longer explanations, see the linked product pages.
DQI stands for Data Quality Intelligence. It is a platform for assessing data quality, enforcing AI usage policies and integrating trusted data into AI and automation workflows. DQI has three product components: DQI Assess, DQI Enforce and DQI Integrate. DQI Enforce and DQI Integrate are currently in development.
Data Quality Intelligence is a category that combines data quality, AI governance and operational policy enforcement. It treats data quality as a live control problem, not a one-off cleansing task, and ties it directly to how AI systems are governed in production.
DQI helps organisations move from informal AI use to governed, auditable AI by combining structured assessment, policy enforcement at the point of AI use and trusted data preparation. DQI Assess is available as the assessment engine; DQI Enforce and DQI Integrate are in development as the policy enforcement and data preparation layers.
DQI Assess is the assessment engine. It scores an organisation across five dimensions: integration architecture, data quality and governance, real-time processing, AI readiness, and governance and compliance. It produces a maturity rating, sector benchmark and remediation plan. A free baseline tier and paid deep-dive tier are available.
DQI Enforce is the in-development AI policy enforcement layer for the DQI platform. It is designed to sit on AI-bound traffic as a governance proxy, evaluate prompts and outputs against organisational policy, apply controls, and produce an audit log of interactions.
DQI Integrate is the in-development data preparation and integration layer for the DQI platform. It is designed to move data between systems, validate and remediate data quality issues in flight, and deliver trusted data into AI, analytics and automation workflows.
DQI Integrate is being designed for iPaaS-style integration use cases where data quality and governance are first-class requirements. For organisations with an existing iPaaS, the intended pattern is to add data quality validation, remediation and governance evidence to existing data flows rather than forcing replacement.
DQI Enforce is designed to intercept AI-bound traffic as a reverse proxy. Prompts and outputs can be evaluated against defined policies before and after model interaction. Policies may block, allow, redact, escalate or require human review, with each decision logged for audit evidence.
DQI supports alignment with recognised AI governance and data quality standards, including ISO 8000-style data quality principles, ISO 27001-style security controls, SOC 2-style reporting and EU AI Act obligations. DQI does not replace formal certification, but it is designed to produce structured evidence that compliance and audit teams need.
AI systems amplify the quality of the data they consume. Poor input data produces unreliable, biased or unsafe outputs, and prompt-level guardrails alone cannot fix that. Treating data quality as a live, governed control, measured, enforced and audited, is foundational to making AI trustworthy in production.