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Good practice Imported

SUPACE — India's Supreme Court AI Case-Assistant, Still Waiting on the Hardware to Scale

India · New Delhi · See the India profile · See the New Delhi profile

Evidence: Descriptive / self-reported Top 88% 33/100 · Ask Evidence Copilot about this practice

India's Supreme Court has run its AI case-research tool SUPACE since April 2021 to extract facts and precedents from case files, but the government's own 2025 parliamentary reply confirms it remains experimental, pending GPU/TPU procurement.

54 million cases
Pending cases nationwide (as of government figures cited)
53.57 ₹ crore
eCourts Project Phase-III AI/blockchain allocation (2023–2027)

Details

Maturity
Pilot
Promoter
Supreme Court of India Registry, with IIT Madras and the National Informatics Centre
Period
2021–present (experimental stage as of 2025)
Keywords
judiciary, court administration, case management

Context

SUPACE (Supreme Court Portal for Assistance in Courts Efficiency) is an AI tool built for the Supreme Court of India's registry, with IIT Madras and the National Informatics Centre, to help judges and legal researchers manage India's court backlog — over 54 million pending cases nationwide as of the government's own figures.

Objectives

The tool ingests case files, extracts the factual chronology and evidence, and surfaces relevant precedents through a chatbot-style interface, aiming to cut manual research time behind each case rather than to participate in judicial decision-making.

Activities

Launched in pilot form in April 2021, the broader eCourts Project Phase-III (2023-2027) has allocated ₹53.57 crore specifically for AI and blockchain integration into case management and scheduling, of which SUPACE is one part, alongside separately developed tools such as the vendor-built Nyaay AI used for defect detection across several High Courts.

Results

SUPACE's current status was set out by the Minister of Law and Justice in a March 2025 written reply to the Rajya Sabha: the tool remains in an experimental testing stage, and its wider deployment depends on procuring GPUs and TPUs that had not yet been acquired. The government's statement explicitly disclaims any role for SUPACE in judicial decision-making, restricting it to research assistance.

Conclusions

Four years after its pilot launch, SUPACE illustrates a transparently disclosed but still-unscaled AI deployment: real, government-run, and publicly reported on, but without independent performance metrics and without the infrastructure needed to move past the experimental stage.

Implementation
Indicative cost
High (€500k–€5M) — eCourts Project Phase-III (2023-2027) allocated ₹53.57 crore (roughly €5-6 million equivalent) for AI and blockchain integration across case management broadly, of which SUPACE is only one component alongside tools like Nyaay AI; SUPACE's own specific budget is not separately disclosed. cost_band is a conservative estimate based on this shared allocation, not a confirmed SUPACE-specific figure.
Time to results
Long (> 3 years) — Piloted from April 2021; as of a March 2025 government statement, still in the experimental testing stage pending GPU/TPU procurement to scale.
Staffing & skills
Supreme Court of India Registry as institutional owner, IIT Madras and National Informatics Centre as technical development partners

Conditions for success

  • Explicitly restricted to research assistance, with no role in judicial decision-making, as stated by government
  • Depends on procurement of GPUs/TPUs to move beyond the experimental stage

Common failure modes

  • Four years after pilot launch, wider deployment is still blocked by unacquired GPU/TPU hardware
  • No independent performance metrics have been published
  • The eCourts Phase-III AI/blockchain budget (₹53.57 crore) is shared across multiple tools, including the separately built Nyaay AI, not allocated solely to SUPACE

Where it fits

Governance type
national judiciary (Supreme Court registry)
Scale
national (India, addressing 54 million pending cases)
Income level
lower-middle-income

Commonly funded by

National / regional programmes

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Data sources

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