1 week ago
How Institutional Partnerships Are Scaling AI Across India
Some groups in India are using AI to help public services work better.
Adalat AI helps courts write down courtroom conversations and manage documents.
Madhi Foundation’s VallamAI helps teachers understand what students need and helps coaches support teachers.
Intelehealth helps health workers connect people in villages with remote doctors.
These tools are being used inside government systems instead of operating as separate apps.
Courts, schools and health programmes help decide how the tools should work.
The organisations also use safeguards to protect court, student and patient information.
The main lesson is that AI spreads further when public institutions help own, govern and use it.
Adalat AI, Madhi Foundation and Intelehealth are embedding AI in courts, schools and public healthcare rather than selling standalone products.
Adalat AI operates in 5,700+ courtrooms across 11 states and has processed more than 3.3 million minutes of courtroom audio.
Madhi Foundation’s VallamAI helps teachers and instructional coaches use assessment, curriculum and classroom-observation data.
Intelehealth supports eSanjeevani telemedicine through government Health & Wellness Centres, reaching roughly 25% of such centres nationally.
The organisations emphasize institutional ownership, privacy safeguards and integration with existing mandates, training systems and workflows.
- Who
- Adalat AI, Madhi Foundation and Intelehealth, working with Indian public institutions and the Pragati: AI for Impact initiative by Meta and the^delta.
- What
- They are scaling AI tools for judicial administration, instructional support in public education and primary healthcare.
- Where
- Across Indian courts, government schools and public Health & Wellness Centres, including systems in Tamil Nadu, Jharkhand, Odisha, Karnataka, Madhya Pradesh and Chhattisgarh.
- When
- Adalat AI’s reported figures are as of June 2026; the article otherwise describes the organisations’ ongoing work through Pragati: AI for Impact.
- Why
- To strengthen existing public-service workflows and expand adoption through institutional ownership, while protecting sensitive data.
Institution-led scaling
Standalone product adoption
How AI reaches users
Institution-led scaling
The organisations work inside existing government mandates, workflows, training systems and facilities, allowing institutions to shape and own the technology.
Standalone product adoption
A standalone product model builds technology first and then seeks individual adopters; the article presents this as more likely to remain dependent on early enthusiasts when institutions do not take ownership.
Data governance
Institution-led scaling
Privacy and governance requirements are built into the partnership from the beginning, including in-house court data, separated student identities and consent-based health data collection.
Standalone product adoption
The article does not provide a specific defence of standalone products’ data practices, but it emphasizes that public institutions may reject technology that does not clearly address custody, misuse and consent risks.
Key facts
- Judicial deployment
- Adalat AI is deployed across 5,700+ courtrooms in 11 Indian states.
- Judicial mandates
- Statewide judicial mandates are reported in Kerala, Sikkim and Andhra Pradesh.
- Adalat AI output
- As of June 2026, it had processed 3.3+ million minutes of courtroom audio and generated 478,000+ judicial documents.
- Reported time savings
- Adalat AI estimated that it saved 10 million minutes of courtroom time.
- Education partnership
- Madhi Foundation works closely with the Tamil Nadu School Education Department and serves as Chief Management Partner for Ennum Ezhuthum.
- Healthcare reach
- Intelehealth’s support for eSanjeevani has expanded to Odisha, Karnataka, Madhya Pradesh and Chhattisgarh and covers roughly 25% of Health & Wellness Centres nationally.
- Data safeguards
- The organisations describe zero-trust court data architecture, separation of student identities from AI interactions and consent-based collection of necessary health information.
Sources
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