OpenAI’s IndQA benchmark shifts the conversation from translation to native, culture-aware reasoning. Here’s what it means for AI product teams, startups, and enterprises,

Published by InteligenAI — 12 November 2025

Introduction

In today’s age of large-language models (LLMs) and generative AI, one major gap persists: language and cultural depth. Most benchmarks, systems and deployments assume English as the default. But with over 80% of the global population not using English as their primary language, this English-centric assumption leaves huge swathes of users, contexts, and markets underserved.

IndQA is a new benchmark from OpenAI designed to evaluate AI systems on reasoning, knowledge, and nuance in Indian languages and context. In this blog, we explain what IndQA is, why it matters for India’s AI ecosystem, and how organizations should be thinking about localization, language-aware AI, and real-world deployment.

What is IndQA?

Here are the key facts:

- IndQA is a benchmark developed by OpenAI to evaluate how well AI models understand and reason in Indian languages and within Indian cultural domains. (OpenAI)

- It comprises 2,278 questions across 12 languages and 10 cultural domains, created in collaboration with 261 domain experts from across India.

- The languages include: Bengali, English, Hindi, Hinglish (code-switching), Kannada, Marathi, Odia, Telugu, Gujarati, Malayalam, Punjabi and Tamil.

- Domains covered: Architecture & Design; Arts & Culture; Everyday Life; Food & Cuisine; History; Law & Ethics; Literature & Linguistics; Media & Entertainment; Religion & Spirituality; Sports & Recreation.

- Rather than simple translation or multiple-choice, each question is natively written in the language, comes with an English translation (for auditability), includes expert-authored rubrics and ideal answers, and expects reasoning, context and cultural nuance.

- IndQA is not primarily a leaderboard comparing models across languages; instead it is a tool to measure progress over time within a model family or configuration. (The Indian Express)

Why IndQA matters — from a market, technology and strategic perspective

1. Market relevance in India

- India is one of the world’s most linguistically diverse markets: 22 official languages, many more dialects, high levels of code-switching (e.g., “Hinglish”).

- India is reported to be the second-largest market for ChatGPT (and similar AI systems) after the US.

- For businesses and enterprise AI deployments in India (and in multilingual, culturally diverse markets globally), being English-first is no longer sufficient. To truly serve Indian users — consumers, businesses, public sector — AI must understand Indian languages, culture, business contexts.

- For companies targeting Indian enterprises (or global enterprises wanting Indian market relevance), adopting language-aware AI is a competitive differentiator.

2. Technology & product implications

- Benchmarks like IndQA highlight that many models still perform poorly when it comes to culturally grounded reasoning in non-English contexts. For example, the fact that many questions were chosen because even state-of-the-art models failed them.

- It signals a shift from “translate from English” → “native in-language reasoning”. For AI product teams, this means training data, prompt design, inference pipelines, test datasets and evaluation metrics must be re-thought.

- For enterprise AI (document intelligence, CRM, chatbots, knowledge agents) this means: if your system only handles English (or simple translation), you will miss context, nuance, regional user behaviours, idioms and localised knowledge. That affects accuracy, user adoption, trust.

- For custom AI systems, domain copilots, RAG frameworks, etc., this reinforces the need to include multilingual, culturally aware layers in architecture — especially for India-based deployments or Indian users.

3. Strategic & global implications

- IndQA sets a precedent: region-/culture-specific benchmarks will become more prevalent (Latin America, Africa, Southeast Asia etc). So building expertise now gives future advantage.

- For global AI companies and service providers, being able to say “we serve Indian languages, Indian cultural contexts” will matter. It’s not just about translation; it’s about reasoning like a native speaker, understanding culture, idioms, domain knowledge.

- From a risk and compliance viewpoint: if your system misunderstands culture, it may mis-interpret, offend, be irrelevant — which hurts trust and adoption.

How should organizations respond?

Here’s a structured approach that organizations can use as a blueprint:

Step 1: Audit your current AI systems for language & cultural coverage

- Identify all systems (chatbots, assistive agents, document-intelligence flows, RAG pipelines) and check which languages they support.

- Check whether your datasets/training incorporate non-English languages, regional idioms, culture-specific references (e.g., Indian regulatory context, Indian legal/finance/insurance domain).

- Check existing evaluation metrics: are you only measuring English-language performance? Are you considering “does the system reason correctly in local context”?

- For example: In India, a claims document may contain local language, local statute references, regional cultural practices. If your insurance-AI only handles English, you risk mis-processing.

Step 2: Define a multilingual-cultural roadmap

- Select target languages based on your clients/market: e.g., Hindi, Tamil, Kannada, Marathi, Bengali.

- For each language: identify domain relevance (insurance, healthcare, public sector, travel, etc.) and cultural nuance (terminology, legal/regulatory frameworks, local idioms).

- Build or source native-language data: prompts, questions, documents, conversations, domain-expert responses. This aligns with how IndQA was built: native prompts + expert rubrics.

- Enhance your evaluation: use native language test sets, rubrics aligned with business outcomes, not just translation accuracy.

Step 3: Integrate into AI architecture

- When building a domain-copilot or RAG system, include multi-language embedding layers, language detection, prompt templates for different languages, fallback/translation logic only when necessary but not as primary path.

- For document-intelligence (OCR, classification, validation) pipelines: include support for Indian scripts (Devanagari, Kannada, Bengali, etc), mixed-language documents (code-mixing).

- Set up monitoring: track output quality by language, region, domain. Evaluate error types in each language (e.g., misunderstanding, missing cultural reference, mistranslation of legal term).

- For clients in India or global clients with Indian operations, offer language-aware service as a differentiator: “We support Hindi, Tamil, Marathi, Kannada for insurance/claims automation.”

- Example: For a general insurer handling claims in India, recognise that claim descriptions may be in English + local language, regulatory forms might be in Hindi, local culture (e.g., local festivals, regional treatments) matters — your AI must handle that.

Step 4: Communicate your language-cultural capability

- On your website, case studies, blog content (like this one) mention your multilingual, cultural-aware AI offering.

- Use keywords: “multilingual AI for Indian enterprises”, “language-aware copilot India”, “AI for Indian languages and culture”, “insurance claims automation Hindi Kannada Tamil”, etc.

- Leverage this benchmark news (IndQA) to show you are aligned with latest research and industry standards — builds credibility.

- Consider partnering with Indian domain experts, linguists, cultural specialists as you scale.

Why India is the proving ground for the next wave of AI?

- India is uniquely positioned. With 22 official languages, deep cultural diversity, and a large tech-savvy ecosystem, India is not just a user of global AI—it can shape how AI is built and reasoned.

- IndQA exemplifies this shift: from English-first → multilingual-cultural intelligence. For India the opportunity is two-fold:
  1. 1. Consume global AI tools — but only if they truly adapt to Indian languages & context
  1. 1. Build AI tools that are native to Indian context, and potentially export that capability globally to other multilingual, culturally rich markets

- For business leaders, this means if your AI is still English-first, you risk being left behind in Indian market deployments, or missing out on deep local adoption.

- For example: A CRM solution built for Indian SMEs must handle Hindi/English mix, local dialects, vernacular phrases context.

Challenges and considerations:

- Data scarcity & quality: While benchmarks like IndQA are important, real-world data in Indian languages (especially domain-specific like legal/regulatory/insurance) is still limited. Organizations need to invest in native-language data collection and annotation.

- Evaluation complexity: Measuring “understands culture” is harder than measuring translation accuracy. Rubric-based evaluation (as used in IndQA) is necessary but resource-intensive.

- Language vs dialect: India has many languages, but equally many dialects, code-mixing behaviours, script issues. Systems must be robust.

- Deployment & user interface: Even if backend supports many languages, UI and user flows must be localised (text, voice, user expectations).

- Ethics, bias and regional fairness: Cultural understanding requires being sensitive to regional norms, values. AI must not assume one culture fits all.

- Scalability for other geographies: While India is a key starting point, the next wave of localisation will target Africa, Latin America, Southeast Asia. The architecture must be designed to scale beyond one locale.

The launch of IndQA marks a significant inflection point: AI for the many, not just the English-speaking few. For businesses in India, for Indian users, for developers and service providers, the message is clear: multilingual, culturally grounded AI is no longer optional — it is essential.

At InteligenAI, we help enterprises build that next-generation AI. Whether it’s document intelligence, multilingual chatbots, domain-copilots, or regionalised RAG systems — we have the experience, the technology, and the strategy to move beyond English-only AI into truly inclusive, high-performance systems.

If your organization is ready to expand its AI capabilities into Indian languages and cultural contexts, reach out to the InteligenAI team today. Let’s explore how our custom-built, multilingual-aware AI solutions can deliver real results in India and globally.orga

Why the launch of IndQA by OpenAI matters for global AI — and why India is key