AI Video for Indian Brands: Hindi and Beyond

Indian brands can now turn one English video into Hindi, Tamil or Telugu in hours instead of weeks. The tools work well enough that the question has moved from "can it be done?" to "will this version embarrass us in Chennai?" The only reliable answer comes from testing each language with native speakers before the media money goes out.

Why does language matter so much for Indian video?

Because nearly every Indian internet user already consumes content in an Indian language. The IAMAI and Kantar Internet in India 2024 report found that 98% of internet users accessed content in Indic languages, and 57% of urban users prefer regional-language content, according to Storyboard18's coverage of the report.

Search is moving the same way. In October 2025 Google added Bengali, Kannada, Malayalam, Marathi, Tamil, Telugu and Urdu to AI Mode.

Teams already use generative AI mainly for speed. In Adobe's 2025 India study of 345 executives, 73% cited greater volume and speed of content ideation and production as a benefit. Multilingual video is where that speed is most tempting and most risky.

What can AI video tools do in Indian languages?

Three kinds of tool cover most needs:

Approach	What it does	Best for	Main risk
AI dubbing	Translates and re-voices an existing video, sometimes with lip-sync	Explainers, product demos, training	Mispronounced names and numbers, unnatural register
AI avatar video	Generates a presenter speaking your script	Frequent updates, internal comms, FAQs	Looks synthetic; needs consent if modelled on a real person
Native shoot with AI support	Real presenter in each language; AI for subtitles, cut-downs and variants	Brand campaigns, emotional or humorous ads	Higher cost and time

Language lists vary by tool and model. ElevenLabs' dubbing documentation lists Hindi, Tamil, Telugu, Marathi, Kannada, Malayalam, Gujarati and Punjabi on its newer dubbing model, with Bengali and Odia available on the older one. HeyGen's video translation page claims "175+ languages" with lip-sync, and its own FAQ says accuracy is strongest for "clear audio and common languages". A language appearing on a list tells you the tool accepts it. It does not tell you the output is good enough for your audience.

The language test scorecard

Score every tool and language on these six tests before a campaign. Use the same 45 to 60 second script for every language, and have two native speakers score each version from 1 (unusable) to 5 (indistinguishable from a good human version).

#	Test	What to check	Script element that tests it
1	Pronunciation	Brand names, product names, place names, prices and dates	Include the brand name twice, one price in rupees and one date
2	Code-mixing	Natural Hinglish, Tanglish and similar mixes; English terms people actually use	Include two English words a native speaker would keep in English
3	Lip-sync	Mouth movement on close-ups, especially at fast speech	One close-up shot of at least 10 seconds
4	Register	Formal or informal address (for example "aap" or "tum" in Hindi), tone for the audience	One direct address to the viewer
5	On-screen text	Correct script rendering, line breaks, font support for Devanagari, Tamil and other scripts	One on-screen caption with the offer or call to action
6	Compliance	AI label in place, consent from anyone whose face or voice is used, claims checked	The final export as it would be published

Set a pass mark before you look at results. A sensible rule is no score below 3 on any test, and 4 or above on pronunciation for anything with a price or a brand name.

How to run this test

A useful first test takes about a week and needs no specialist team.

1. Write one script. Keep it to 45 to 60 seconds in English and build in all six script elements from the scorecard: the brand name twice, a rupee price, a date, two English words a native speaker would keep, a direct address to the viewer, a close-up of at least 10 seconds and one on-screen caption.
2. Pick the languages. Start with Hindi and one regional language your customers actually use, chosen from your sales or enquiry data rather than from the tool's list.
3. Pick two or three tools. Produce every language in each tool from the same source video, using default settings first so the comparison is fair.
4. Recruit two native speakers per language. They should not have worked on the script. Ask them to score each version separately before they compare notes.
5. Log more than scores. For each version, record the six scores, the time from upload to final export and the cost in rupees, including any manual fixes.
6. Apply the pass mark. Any version below the mark goes back for fixes or is dropped. Do not average a failed pronunciation score away with good lip-sync.

Use one row per tool and language:

Tool and language	Pronunciation	Code-mixing	Lip-sync	Register	On-screen text	Compliance	Time	Cost (₹)
(Tool), Hindi
(Tool), (regional language)

Keep the side-by-side clips with the scores. They are the quickest way to show a brand head why one language is ready and another is not.

What do India's AI-labelling rules require?

Platforms must label AI-generated video, and brands should plan for that label rather than fight it. This section is a marketer's summary, not legal advice.

The Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 were amended by G.S.R. 120(E), dated 10 February 2026. MeitY's consolidated text defines "synthetically generated information" as audio, visual or audio-visual information "artificially or algorithmically created, generated, modified or altered" so that it "appears to be real, authentic or true" and depicts a person or event in a way likely to be "perceived as indistinguishable from a natural person or real-world event".

Three duties matter for brands:

• Labels and metadata. An intermediary offering tools that create such content must ensure it is "prominently labelled", or carries "a prominently prefixed audio disclosure" for audio, and is embedded "with a permanent metadata or other appropriate technical provenance mechanisms, to the extent technically feasible".
• Declarations. Significant social media intermediaries must "require users to declare whether such information is synthetically generated information" before it is published, and verify those declarations.
• Impersonation. Tools must not allow content that "falsely depicts or portrays a natural person ... by misrepresenting, in a manner that is likely to deceive, such person's identity, voice, conduct". Do not make an avatar or voice clone of anyone without their written consent.

The definition excludes some routine uses, including the use of computer resources "solely for improving accessibility, clarity, quality, translation" without "generating, altering, or manipulating any material part" of the original. Whether a dubbed video with lip-sync falls inside or outside that exclusion is a question for your lawyer. Lip-sync changes the picture, so assume a label is needed.

Clinics and doctors face a stricter rule. Clause 7.2 of the NMC's October 2026 guidelines prohibits AI-generated promotional campaigns for commercial interest, requires any compliant AI content to "carry the source mark", and bars AI from creating or manipulating "a patient's image, testimonial, voice or clinical outcome".

Where does AI video work, and where does it fail?

It works best where the message is informational and the audience is forgiving of a slightly synthetic voice: product explainers, how-to videos, FAQs and internal training. It fails where trust, emotion or cultural nuance carry the message: humour, festive campaigns, testimonials, and anything in healthcare or finance. Regional languages with fewer training examples tend to show weaker pronunciation and idiom, which is exactly why the scorecard tests each language separately.

A dubbed video that sounds almost right in Bhojpuri can do more harm than an honest Hindi original.

Who should own AI video in a marketing team?

The content or brand lead should own the decision to publish, with native-language reviewers scoring every version, legal or compliance checking labelling and consent, and the performance team reporting results by language. The tool vendor should never be the one deciding a language is good enough.

When should you dub, use an avatar or shoot natively?

Match the method to how much the audience needs to trust the person on screen.

• Dub existing explainers, demos and training videos once the language passes the scorecard.
• Use avatars for high-volume, low-emotion content such as product FAQs or internal updates, labelled clearly.
• Shoot natively for brand campaigns, humour, festive work and anything in healthcare or finance where trust is the product.

My view

Having directed national campaigns from TVC production to digital, my view is that a D2C brand starting regional video this quarter should begin by dubbing its existing explainers and product demos into one or two languages and scoring them. Dubbing gives the fastest evidence for the least money. Native shoots can then be kept for the campaigns where trust or humour carries the message, once the scorecard shows which languages the tools handle well enough to publish.

The trade-off is that the scorecard slows the first video down. Native reviewers and two-tool testing add a week or two before anything goes live, but they stop a brand publishing content that embarrasses it in the very regions it is trying to win.

Run the scorecard before the next campaign

Write one test script with the six elements built in, produce it in two languages with two tools, and have native speakers score it. Keep the scorecard with the campaign file so the next team starts from evidence, not a vendor's language list.

For why language choice is a strategy decision rather than a translation task, see the Bharat-first marketing principles.