Case study · Justice
120,000 statutes,in one reasoning graph.
A municipal bureau of justice responsible for reviewing the legality of government documents and regulations. That review needs deep legal expertise and long experience, so the knowledge barrier is high and the number of qualified reviewers was never sufficient, which left heavy backlogs and sustained workload pressure. Laws and regulations are revised frequently, which makes learning and compliance maintenance expensive. Manual review results moved with personal judgment and with fatigue, so quality was inconsistent and oversight was possible.
Constraints and answers
Too few reviewers, and the law keeps changing.
The system that carries 120,000 statutes
High professional threshold
Legality review requires deep legal expertise and long experience, which is a high barrier to entry for the work.
Limited human resources
There were not enough qualified reviewers, which left heavy backlogs and severe workload pressure.
Frequent legal updates
Laws and regulations are revised often, and the cost of learning them and maintaining compliance is high.
Quality variability
Manual review results were subject to personal judgment differences and to fatigue, so quality was inconsistent.
Legal knowledge graph
Over 120,000 legal statutes and more than 80,000 case references integrated into interconnected networks for reasoning and retrieval.
Multi-model intelligent review engine
Hybrid algorithms for legality assessment, with attention-mechanism models detecting conflicting clauses and proposing revisions.
Full-process workflow management
Intelligent task allocation, progress monitoring, automated alerts, and feedback loops that keep improving the quality of the output.
Statutes and cases in one network, and an engine that reasons across it.
The results
120,000 statutes and 80,000 cases, reasoned over together.
- 120,000
- LEGAL STATUTES INTEGRATED
- Held in the knowledge graph for reasoning and retrieval.
- 80,000
- CASE REFERENCES INDEXED
- Indexed for reasoning and retrieval across every legal domain.
- CONFLICT CLAUSE DETECTION
- Automated detection in place of single-reviewer manual judgment.
- QUALITY OPTIMIZATION
- Feedback loops that take fatigue-driven inconsistency out of the result.
Consistency here is not efficiency. It is whether two identical documents get the same answer.
The stack
What was deployed
120,000 statutes and 80,000 case references in one interconnected network, which is what makes reasoning across them possible.
Hybrid AI algorithms for legality assessment, combining several models for a complete reading of a document.
Attention-mechanism models identifying conflicting clauses and generating revision suggestions with precision.
Task allocation, progress monitoring, automated alerts, and continuous quality optimization.
Примечание о возможностях
Возможности, компетенции и примеры внедрений, представленные на этом сайте, отражают совместную работу ключевой инженерной команды Amanah. Некоторые проекты были реализованы через предыдущие или аффилированные организации. Суверенные внедрения конфиденциальны по мандату, поэтому отдельные клиенты не называются. Соответствующие референсы подлежат проверке в рамках NDA.
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