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AI Powered Courtroom Transcription and Workflow Automation for Timely Justice

AI Powered Courtroom Transcription and Workflow Automation for Timely Justice

India flag

India

Justice and Governance

High replicability and adaption

Implementing Organisation

Adalat AI

India, Multiple states (Pan India deployment), Multiple districts

Civil Society

Implementing Point of Contact

Utkarsh Saxena

Co founder and Chief Executive Officer

Contributor of the Impact Story

Adalat AI

Year of implementation

2023

Problem statement

Judicial systems in many countries face severe delays due to administrative ottlenecks within courtrooms rather than legal complexity alone. In India, over 50 million cases are pending, with average case resolution times stretching over a decade. A critical contributor to this backlog is the reliance on manual, paper based courtroom processes. Courts face an acute shortage of trained stenographers, resulting in delays in recording witness testimony, dictation of orders, and preparation of judgments. Proceedings are often documented manually, creating risks of errors, omissions, and inefficiencies. Case workflows during live hearings are uncoordinated, requiring judges to manage clerical tasks alongside adjudication. Litigants and lawyers frequently lack timely access to case updates, leading to adjournments, uncertainty, and reduced trust in the justice system. These inefficiencies disproportionately affect undertrial prisoners, low income litigants, and individuals without legal representation, for whom procedural delays translate into prolonged detention, financial hardship, and denial of timely remedies. The challenge is to modernize courtroom operations in a way that strengthens institutional capacity, preserves judicial discretion, and can scale across jurisdictions without requiring extensive new hardware or staffing.

Impact story details

Adalat AI is a nonprofit justice tech organization building an end to end AI powered digital infrastructure for courts in the Global South. Founded in India, Adalat AI works in close partnership with High Courts, State Judicial Academies, and court administrations to modernize judicial workflows and reduce systemic delays in justice delivery. Judicial systems across many low and middle income countries face chronic backlogs not because of legal complexity alone, but due to administrative and logistical inefficiencies. Manual transcription, paper based documentation, fragmented case workflows, and limited access to case information consume judicial time and disproportionately harm undertrial prisoners, low income litigants, and marginalized communities. Adalat AI addresses these bottlenecks by embedding domain trained AI tools directly into daily courtroom operations. Its platform automates high volume clerical tasks such as real time transcription of proceedings, workflow management during hearings, digitization and navigation of legal records, and citizen access to case updates. The objective is to strengthen institutional capacity so judges and court staff can focus on adjudication rather than administration. Adalat AI-s tools are currently live in over 4,000 courtrooms across nine Indian states, representing approximately 20 percent of India-s judiciary. Several High Courts have formally integrated or mandated the use of Adalat AI within their court systems, supported by structured training programs delivered through judicial academies. The organization is recognized under the Government of India-s IndiaAI Mission as one of the country-s leading generative AI initiatives, and the only nonprofit in that cohort. Adalat AI-s impact is being rigorously evaluated through a large scale randomized controlled trial led by J PAL at MIT, measuring effects on court efficiency, case resolution speed, and judicial quality. The organization-s long term goal is to enable scalable, context appropriate AI adoption for justice systems across the Global South.

AI Technology Used

Natural Language Processing (also incorporating Speech Recognition
Machine Learning)

Key Outcomes

Efficiency

Productivity Access

Reach Inclusion

Equity Accuracy

Quality Improvement User Experience

Satisfaction

Impact Metrics

Accuracy of legal transcription in live court settings

Post-Implementation

0

Implementation Context

Scaled

India. Deployed across nine states including Kerala, Karnataka, Andhra Pradesh, Odisha, Madhya Pradesh, Delhi, Punjab and Haryana, and Bihar.

Primary users include judges, court staff, and lawyers across district and subordinate courts. Indirect beneficiaries include millions of litigants, particularly undertrial prisoners, low income individuals, and first time court users who benefit from faster proceedings and improved access to case information.

Key Partnerships

State High Courts and court administrations State Judicial Academies Government of India programs including IndiaAI Mission Academic partners for impact evaluation

Replicability & Adaptation

High

Global Relevance: The Shared Colonial Context -Common Law Heritage & Shared Challenges Adalat AI-s model is uniquely scalable because of the shared legal history across much of the Global South. Many nations in Commonwealth Africa (e.g., Kenya, Nigeria, Ghana, Uganda) and South Asia operate under legal systems rooted in British Common Law. Structural Similarity: These judicial systems share identical procedural codes, courtroom hierarchies, and legal terminology. A "writ petition" or an "interlocutory application" means the same thing in New Delhi as it does in Nairobi or Lagos. The "Paper Burden": The administrative bottlenecks-manual transcription, heavy reliance on physical files, and procedural formalism-are not unique to India but are characteristic of post-colonial bureaucracies that have not yet fully digitized. Resource Constraints: Unlike Western legal-tech built for high-resource environments, Adalat AI is engineered for courts with limited hardware, internet intermittency, and high caseload-to-judge ratios. Replicability in Africa The potential for impact in Africa is high due to these parallel structures: Direct Portability: Because the underlying legal frameworks are so similar, the AI models (trained on millions of Indian legal documents) require significantly less fine tuning to adapt to African jurisdictions compared to models trained on US or EU data. South-South Cooperation: This represents a shift from "North-South" aid to "South South" technical partnership. It offers a model where technology developed in a high volume, cost-constrained environment (India) is exported to peers facing nearly identical challenges, avoiding the pitfalls of expensive, ill-fitting Western software.

* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.