IndQA and the New Standard for AI Support in SaaS

Most companies still judge “multilingual AI” the wrong way: they test whether a model can translate a sentence, or pick a multiple-choice answer, then call it ready for production.

That shortcut breaks the moment your product hits real customers—especially in digital services where users don’t speak like textbooks. They code-switch. They reference local laws, pop culture, food, sports, or historical events. They expect the assistant to understand context, not just vocabulary.

OpenAI’s IndQA benchmark (released November 2025) is a strong signal that the industry is finally taking that gap seriously. It doesn’t just ask whether a model can “speak Hindi” or “translate Tamil.” It tests whether an AI system can reason through culturally grounded questions across 12 Indian languages, with 2,278 expert-written prompts spanning 10 domains. If you’re building or buying AI for customer support automation, search, onboarding, or in-app assistants in the U.S., IndQA matters more than it sounds—because the U.S. market is multilingual, multicultural, and increasingly expectation-heavy.

This post is part of our “How AI Is Powering Technology and Digital Services in the United States” series, and the takeaway is simple: better evaluation creates better AI behavior, and better AI behavior creates better digital service experiences.

IndQA is about context, not translation

IndQA’s core contribution is straightforward: it evaluates whether AI can answer reasoning-heavy, culturally specific questions written natively in Indian languages—rather than treating language as a conversion problem.

That distinction matters because customer communication in SaaS and digital services is rarely “translate this paragraph.” It’s more like:

• “My refund didn’t show up—does that mean my bank rejected it?”
• “The app says I’m ineligible. Which document is missing?”
• “Why did my account get flagged? I didn’t do anything.”

These are context and policy questions. Users are asking for explanations, next steps, and reassurance. A system that’s only good at translation can still fail the job.

IndQA pushes on exactly the kinds of failure modes product teams see in the real world:

• Ambiguity (what the user meant, not just what they wrote)
• Local context (laws, norms, institutional processes)
• Cultural references (media, sports, history, everyday life)
• Code-switching (like Hinglish, which IndQA includes explicitly)

A multilingual assistant isn’t “accurate” if it speaks your language but misunderstands your world.

Why a U.S. SaaS company should care about an India-focused benchmark

At first glance, IndQA looks region-specific. But the U.S. implications are immediate.

The U.S. digital economy is multilingual by default

American tech products serve users who speak Spanish, Chinese, Tagalog, Vietnamese, French, Arabic, Hindi, and many others—often in the same metro area, sometimes within the same household. Even when users speak English, they bring culturally specific expectations and references.

So while IndQA is built for Indian languages and culture, it represents a broader shift that U.S. companies need: language evaluation must include culture, reasoning, and real-life tasks.

Customer support is the biggest “AI reality check”

Sales demos make AI look polished. Support tickets make it honest.

Support interactions are where hallucinations, tone issues, and policy misinterpretations cause measurable damage:

• Higher handle time (AI creates more back-and-forth)
• Wrong resolutions (refunds, account actions, eligibility)
• Compliance risk (incorrect legal or policy guidance)
• Brand risk (tone-deaf responses)

IndQA’s rubric-based approach is a blueprint for how support teams can evaluate AI in a way that matches the stakes.

Benchmarks shape product roadmaps—even if you never read the paper

The U.S. tech ecosystem runs on vendors and platforms. When labs build better benchmarks, they train and tune models differently. That improves the models you deploy inside:

• AI customer support automation
• AI search and retrieval
• In-app help and onboarding
• Knowledge base assistants
• Agentic workflows (billing, scheduling, account updates)

If you sell digital services, you’re downstream of evaluation quality.

How IndQA works (and why rubric grading is the real lesson)

IndQA evaluates model responses using rubrics written by domain experts for each question. Each rubric includes criteria, weighted by importance, and responses are graded against those criteria.

This approach is more aligned with how businesses actually measure success.

A practical example: in support automation, a “good” answer isn’t the one that sounds fluent. It’s the one that:

1. Identifies the user’s intent correctly
2. Gives the correct policy outcome
3. Lists the right next steps
4. Avoids unsafe advice
5. Uses the right tone for the scenario

Rubrics force clarity: you define what “correct” means.

What’s especially useful: adversarial filtering

IndQA used adversarial filtering: questions were tested against strong models available at the time (including GPT‑4o, OpenAI o3, GPT‑4.5, and partially GPT‑5) and only kept when a majority of them failed.

That’s a big deal for anyone building evaluation suites internally. Many teams accidentally create “easy tests” where every vendor looks good. IndQA is designed to preserve headroom, meaning it can still measure improvement.

If you want your AI support automation to actually get better over time, your evals need headroom too.

What IndQA teaches U.S. teams about deploying AI in digital services

IndQA is a benchmark, not a product. Still, it offers clear operational lessons for U.S. SaaS and digital service leaders.

1) Treat language as a product surface, not a checkbox

If your roadmap says “add Spanish” or “add multilingual support,” you’re not done when the UI strings translate.

Here’s what “language as a product surface” looks like:

• Localized help content that matches actual user scenarios
• Regional policy logic (returns, taxes, eligibility, data rights)
• Tone and formality rules by segment
• Local examples in onboarding
• Escalation paths when the model is uncertain

IndQA’s domain coverage—everyday life, law & ethics, media, history—mirrors how real users frame their questions.

2) Build an evaluation harness before you scale automation

If you’re rolling out AI to handle more tickets, chats, or emails, do this first:

1. Collect 200–500 real interactions across your top issue categories
2. Redact sensitive data (names, payment details, identifiers)
3. Write a rubric per category (not per prompt) that defines:
   • Required facts
   • Prohibited claims
   • Escalation triggers
   • Tone requirements
4. Score AI responses automatically where possible, and spot-check weekly

IndQA’s rubric-first design isn’t academic. It’s a production pattern.

3) Measure “helpfulness” with outcomes, not vibes

Teams often grade responses by whether they “sound good.” That’s how you ship confident nonsense.

Better metrics for AI in customer communication:

• Resolution rate (did the user get to the right outcome?)
• Deflection quality (did the user avoid human support and succeed?)
• Time-to-resolution
• Escalation accuracy (did it escalate when it should?)
• Policy compliance rate

A sentence I keep coming back to: fluency is cheap; correctness is expensive.

4) Plan for code-switching and mixed-language inputs

IndQA includes Hinglish because it matches real conversational behavior. U.S. users do the same thing—Spanglish is the obvious example, but mixed-language messages show up across communities.

If your assistant fails whenever a user blends languages, you’ll see:

• More clarifying questions
• More wrong intent classification
• More fallbacks to English

A simple fix that works: store common code-switched phrases as training/eval examples in your test set, and grade for intent detection, not just translation.

“People also ask” questions, answered plainly

Does IndQA make models better, or just measure them?

IndQA primarily measures. But measurement changes behavior: once a benchmark exists, model developers optimize toward it, and product teams can detect regressions earlier.

Can a U.S. startup use IndQA directly?

If you support Indian languages, yes—IndQA can be a strong external reference point. Even if you don’t, you can adopt the method: expert-written prompts, rubric grading, and adversarial “headroom” tests.

Why are older multilingual benchmarks less useful now?

The issue is saturation: many top models cluster near high scores on older tests, so the benchmark can’t separate “pretty good” from “production-ready.” IndQA was built specifically to restore that separation.

Where this goes next for U.S. digital services

IndQA is a reminder that the next wave of AI differentiation in the United States won’t come from adding another chatbot widget. It’ll come from trustworthy communication at scale: the ability to answer complex queries in real time, in the user’s language, with the right cultural and policy context.

If you’re responsible for customer experience, product growth, or support ops, here’s the move I’d make in early 2026 planning: budget for evaluation the same way you budget for model usage. Benchmarks like IndQA show why. The teams that can measure nuance will ship automation that actually reduces cost while improving user outcomes.

If you’re building AI into a digital service, what’s the hardest category of questions your users ask—refunds, eligibility, compliance, technical troubleshooting—and do you have a rubric that defines “correct” today?