Case study · B2B marketplace · Translation
IndiaMART bridged the language barrier by translating 20 million product listings using Bedrock.
IndiaMART · B2B marketplace
ML translation pipelines that lacked context, needed manual review, and ignored regional SEO. We replaced them with a context aware pipeline.
20M
Product listings translated
40%
Lower translation cost
1.5M
Listings supported with language options
The opportunity
Translation that was accurate word by word, and wrong in context.
IndiaMART connects 19.8 crore buyers with 11 crore products from 80 lakh verified suppliers. Reaching those buyers in their own language meant translating product catalogues at a scale where the existing ML models' lack of context became expensive: every batch needed significant manual intervention to be usable.
The models also overlooked region-specific SEO keywords, so translated pages ranked poorly in local search-and terminology drifted between titles, descriptions, technical terms and marketing copy, because nothing held them consistent.
ML models lacked context
Translations were literal, missing the cultural relevance the listing needed.
Heavy manual intervention
Every batch required human correction, driving operational cost up.
Regional SEO keywords overlooked
Translated pages ranked poorly in local search results.
Terminology drifted
Titles, descriptions and technical terms stayed inconsistent across languages.
The impact
20 million listings translated with no human in the loop.
Context aware at 20M scale
LLMs on Amazon Bedrock handle contextual translation of the full catalogue without manual intervention.
SEO built into the pipeline
A custom framework injects region-specific keywords, so translated pages rank in local search.
40% off translation cost
Removing the manual correction pass is where the cost reduction comes from.
New products launch translated
Accurate multilingual content ships with the listing rather than trailing it.
The stack
What it runs on.
- Model layer
- Amazon Bedrock, large language models
- Ingestion
- Product titles and descriptions extracted to Amazon S3
- SEO enrichment
- Custom region-specific keyword framework
- Delivery
- Processed data back to S3 for platform integration
The road ahead
The catalogue speaks the language. Next is the rest of the journey.
With listings translated in context, the same pipeline extends outward-more languages, more of the buyer journey beyond the product page, and SEO measurement per market.
- 01
More regional languages in production
- 02
Translated content beyond the listing
- 03
Per-market SEO measurement
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