Multilingual & Vernacular SEO

Hreflang tells Google that several URLs are the same content for different audiences. Done correctly, the right market surfaces for the right user. Done incorrectly — and it very often is — your markets compete, the wrong currency and language appear, and conversion drops for reasons nobody traces back to a tag.

The frequent failures: annotations that are not reciprocal, so Google ignores them entirely. Missing self-references. Region codes used where language codes belong. Hreflang pointing at URLs that redirect. Clusters broken because one market launched late and nobody revisited the others.

Underneath sits a structural decision — subdirectories, subdomains or country domains — that is expensive to reverse. Getting it right at the outset is worth considerably more than fixing tags later.

Indian-language search has grown substantially and behaves unlike English search. Queries are longer and more conversational. Voice input is common, which changes phrasing again. And a large share of users type Indian languages in Latin script — transliteration — which your keyword research will miss entirely if you only look at native script.

Machine translation performs badly here. Translated pages read as translated, use terms nobody searches, and rank poorly. What works is establishing what people in that language actually search for, then producing content natively rather than converting English pages.

International architecture

Subdirectory, subdomain or ccTLD decisions with the trade-offs made explicit, since this choice is costly to reverse.

Hreflang implementation and repair

Building or fixing bidirectional clusters, with automated monitoring so a late market launch does not silently break the set.

Market cannibalisation

Diagnosing and resolving cases where your own regional sites compete, most commonly across English-language markets.

Localisation over translation

Keyword research conducted natively per language, so pages target real queries rather than translated English ones.

Transliteration research

Capturing Latin-script variants of Indian-language queries, which conventional keyword tools handle poorly.

Indian-language content production

Native content in major Indian languages, written rather than machine-translated.

Technical language signals

Correct lang attributes, character encoding, script rendering and font loading — unglamorous and frequently wrong.

Geotargeting configuration

Search Console targeting, server location and CDN behaviour, aligned so signals do not contradict each other.