Digital Analytics of Judicial Practice — Kazakhstan's AI Case-Outcome Tool, Scaled to Every Court

Since 2022, Kazakhstan's Supreme Court has run 'Digital Analytics of Judicial Practice,' an AI service that searches and structures millions of court decisions so judges and litigants can see how similar disputes were resolved before a case is argued. Early versions supported private bailiffs' applications to restrict debtors' foreign travel and AI-assisted child-alimony collection applications.

The tool aims to give judges and litigants visibility into precedent before hearings, support routine administrative-offence rulings (such as traffic violations, from July 2024) with a judge retaining final review, edit and sign-off, and feed real-time performance data into the Supreme Court's Situational Center.

An 18-month pilot ran in the courts of Astana, Zhambyl, Karaganda and Mangystau before the Supreme Court's Judicial Administration scaled the system to every court in Kazakhstan in May 2026. The platform now processes roughly 15 million machine-readable data units, up from 4 million at launch, handling up to 1,000 simultaneous queries in Kazakh and Russian.

In 2022, of 156,000 bailiff travel-restriction submissions, the system processed 151,000, with judges authorising 126,000 and rejecting 4,700; over 8,000 AI-assisted alimony applications were also registered that year. During the pilot, settlement rates in the four pilot regions reportedly exceeded the national average once parties could see precedent before hearings. The Situational Center now tracks around 850 judicial performance indicators in real time.

Officials describe the tool as advisory only, with all rulings remaining the legal responsibility of a human judge. The Judicial Administration has flagged data protection and state-secrecy safeguards as ongoing requirements as the rollout extends into administrative and, eventually, criminal proceedings. No independent, controlled impact study has been published — the reported figures are self-reported administrative data.