India is one of the few advertising markets where a single campaign routinely needs to speak several languages, and where getting the second or third one slightly wrong is worse than not attempting it. Clumsy Hindi does not read as a company making an effort — it reads as a scam. This covers what AI tools genuinely handle, what they quietly break, and how to structure a multilingual campaign.
The failure that shows on the image, not in the copy
Devanagari, Tamil, Telugu and Bengali scripts have joining behaviour, conjunct characters and vowel marks that attach above and below the line. Latin text has none of that. When an AI image tool draws text onto a generated scene, the rendering pipeline was almost certainly built and tested on Latin script. Three things go wrong, in order of frequency.
Conjuncts broken apart
Characters that should combine into a single ligature are drawn separately. The letters are technically correct and the word is unreadable — the equivalent of English printed one letter per box.
Vowel marks misplaced
Matras floating above the wrong consonant, or clipped by the line box. Native readers see it instantly; nobody else does, which is exactly why it ships.
Font substitution
The display font has no glyphs for the script, so the system silently falls back to a default. The headline renders in a face that looks nothing like the brand and often breaks the layout.
💡 Test this before trusting any tool with regional creative. Generate one image with a Hindi or Tamil headline and have a native reader look at it. Not a translator checking the words — a reader checking whether it looks like something a real company published. Those are different tests and only the second catches rendering faults.
The structural fix, if you are evaluating tools, is to ask how text gets onto the image. If the image model generates the text as part of the picture, it will be wrong in any script — image models cannot spell in English either. If the tool generates the scene and then draws the headline as real text through a layout engine in a real font, script handling becomes a font question rather than an impossible one.
Translation is not localisation
Even with correct rendering, translated copy underperforms written copy. English ad copy leans on compression and wordplay that does not survive the trip — "Stop guessing. Start selling." becomes two flat imperatives with none of the rhythm.
There is also a register problem specific to India. Formal Hindi reads as government communication; the Hindi people actually speak includes English words that a purist translator will replace. A translated ad frequently lands in the wrong register and feels distant.
The practical approach is to give the AI the offer and the audience rather than the English sentence, and have it write in the target language from scratch. Any tool offering only a "translate" button is doing the weaker of the two jobs.
Should you split the campaign by language?
Usually yes — and the reason is about measurement more than delivery.
- 1Run two languages in one ad set and Meta optimises toward whichever creative gets early traction. That is often the English one, because it reaches a broader initial slice. The Hindi creative then starves before it has been fairly tested, and you conclude regional does not work when you never ran it.
- 2Split into two ad sets and each language gets its own budget and its own learning. You find out what Hindi actually costs per result rather than assuming.
- 3The cost of splitting is the platform minimum, which applies per ad set. A conversion objective sits at roughly three times the impression floor, so two languages means budgeting for two funded ad sets before Meta accepts the campaign.
If your budget cannot carry two properly funded ad sets, do not split into two starved ones. Pick the language your customer is most likely to buy in, run it properly, and add the second when the budget supports it.
How Meta language targeting actually behaves
Misunderstood often enough to be worth stating plainly. Meta's language targeting filters on the language a person has set on Facebook or Instagram, not the languages they speak. An enormous number of Hindi, Tamil and Telugu speakers use their phone interface in English.
💡 So targeting "Hindi" as a language will exclude a large part of your actual Hindi-speaking audience. For most Indian campaigns the better structure is to leave language targeting off entirely and let the creative do the filtering. A Hindi ad is self-selecting — people who do not read it scroll past — and you reach everyone, paying only for the ones the creative speaks to.
Match the destination to the language
A common and expensive mismatch: regional-language ad, English landing page. You bought the click and threw it away at the door.
If you cannot produce a Hindi or Tamil page, that is a real argument for running regional ads into WhatsApp rather than a website — the conversation continues in the language the ad started in, with a person on your side of it. This is a large part of why click-to-chat is so much more effective in India than in Western markets, and why a tool that treats WhatsApp as an afterthought fits badly here.
A workable structure
One ad set per language
Each funded above the platform minimum. Same objective and geography, different creative and copy.
No language targeting
Let the creative filter. You reach English-interface regional speakers you would otherwise exclude.
Written, not translated
Give the tool the offer and the audience, not the English sentence.
Matched destination
Regional ad to a regional page, or to WhatsApp. Never regional ad to English page.
Judged separately
Compare cost per result per language. They will differ, and the difference tells you where the next rupee goes.
Copy written in the language, not run through a translator
Adyft writes ad copy for the market it addresses and draws headlines as real text over the image, so the script renders correctly. From ₹1,999/month, 14-day free trial.
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