RESEARCH RELEASE
Introducing IndQA
A new benchmark designed to evaluate how well AI models understand and reason about questions that matter in Indian languages, across a wide range of cultural domains. Published November 3, 2025.
Unlocking Global AI Potential: The IndQA Impact
IndQA addresses critical gaps in AI evaluation, ensuring models understand and resonate with diverse global cultures, starting with India.
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Global Population Not Primary English
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Official Indian Languages Supported
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Culturally Nuanced Questions
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Domain Experts Engaged
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Comprehensive AI Evaluation
IndQA evaluates knowledge and reasoning about Indian culture and everyday life in Indian languages. It spans 2,278 questions across 12 languages and 10 cultural domains, created in partnership with 261 domain experts from across India. Unlike existing benchmarks, it is designed to probe culturally nuanced, reasoning-heavy tasks that existing evaluations struggle to capture.
It covers a broad range of culturally relevant topics, such as Architecture & Design, Arts & Culture, Everyday Life, Food & Cuisine, History, Law & Ethics, Literature & Linguistics, Media & Entertainment, Religion & Spirituality, and Sports & Recreation.
IndQA Evaluation Flow
User-Assistant Conversation
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Candidate AI Response
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Rubric-Based Expert Scoring
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Final Score Calculation
Each response is graded against criteria written by domain experts for that specific question. The criteria spell out what an ideal answer should include or avoid, and each one is given a weighted point value based on its importance. A model-based grader checks whether each criterion is met.
Rigorous Development Methodology
Our development methodology focused on capturing true cultural nuance and robust AI challenge. This involved a multi-stage process to ensure the highest quality and relevance of the benchmark.
- Expert-authored questions: We worked with partners to find experts in India across 10 different domains. They drafted difficult, reasoning‑focused prompts tied to their regions and specialties.
- Adversarial filtering: Each question was tested against OpenAI’s strongest models at the time of their creation (GPT‑4o, OpenAI o3, GPT‑4.5, and GPT‑5). We kept only those questions where a majority of these models failed to produce acceptable answers.
- Detailed Criteria: Along with every question, domain experts provided criteria used to grade the model response, similar to an exam rubric for an essay question.
- Ideal answers + review: Experts added ideal answers and English translations, followed by peer review and iterative fixes until sign‑off.
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Indian Domain Experts Authored Content
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Case Study: Nuance in Context
IndQA’s strength lies in its ability to probe deeply into cultural contexts, requiring complex reasoning beyond simple factual recall. This ensures AI models can genuinely understand and interact with diverse human experiences.
Example (Bengali - Literature & Linguistics):
Prompt: ‘দণ্ডক থেকে মরিচঝাঁপি’ উপন্যাসের লেখক নিম্নবর্ণের পুরুষ ও নারীদের দণ্ডকারন্যে পুনর্বাসন পরবর্তী জীবন কিভাবে দেখিয়েছেন? দণ্ডকারণ্যে পুনর্বাসন কি সরকারী উদাসীনতার ফল? পরিবর্তিত প্রাকৃতিক পরিবেশের সাথে উদ্বাস্তুরা কিভাবে মানিয়ে নিয়েছিল?
English Translation: How did the writer of Bengali novel ‘Dandak Theke Marichjhanpi’ depict the post-rehabilitation lives of lower caste men and women? Was the rehabilitation in Dandakaranya a result of governmental indifference? What was its relation with the new natural landscapes?
This illustrates the intricate historical, social, and environmental reasoning required, ensuring models grasp the true complexity of human narratives.
Calculate Your Potential AI ROI
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Your Enterprise AI Implementation Roadmap
A structured approach to integrating advanced AI into your operations, from discovery to optimization.
Phase 1: Discovery & Strategy
In-depth analysis of your current operations, identification of AI opportunities, and development of a tailored strategy aligned with your business objectives.
Phase 2: Pilot & Proof-of-Concept
Deployment of a small-scale AI pilot project to validate technology, demonstrate value, and gather initial performance data.
Phase 3: Integration & Scaling
Seamless integration of AI solutions into your existing enterprise systems and scalable rollout across relevant departments or functions.
Phase 4: Monitoring & Optimization
Continuous performance monitoring, iterative improvements, and strategic scaling to maximize long-term ROI and competitive advantage.
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