Cross-lingual similarity evaluation for rare Indian pairs.
A global AI research lab needed cross-lingual semantic textual similarity evaluation for Santali and Oriya paired with Hindi, languages where the pool of trained evaluators is extremely thin.
Santali to Hindi, Oriya to Hindi - 5,000+ prompts evaluated - Same-day correction across 8 files
110,000+
native linguists and AI data contributors
· Founder-reported combined network · 4 Oct 2026
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare, indigenous and low-resource languages
1,000+
organizations served since 2015
Measured outcomes
Cross-lingual similarity evaluation
The project
Cross-lingual similarity evaluation
- Client
- confidential global AI research lab
- Service
- Cross-lingual semantic similarity (XSTS) evaluation
- Languages
- Santali to Hindi, Oriya to Hindi
- Volume
- 5,000+ prompts evaluated
A global AI research lab needed cross-lingual semantic textual similarity evaluation for Santali and Oriya paired with Hindi.
Santali has roughly 7.5 million speakers but an extremely limited pool of trained linguists, so scoring similarity across domains demanded both deep language expertise and evaluation training.
The problem to solve
Scoring semantic similarity between a source and its translation is not translation work: it needs evaluators trained in the methodology and fluent enough to judge meaning as well as words.
For a language like Santali, the constraint is supply: very few linguists combine native fluency with evaluation training, so quality control has to be tight from the first batch.
What MoniSa changed
MoniSa deployed native Santali and Oriya linguists validated for dominant proficiency in both source and target, and shared consolidated linguist feedback before production began.
Validated native linguists
Evaluators were validated for dominant proficiency in both the source and target language.
Feedback before production
Consolidated feedback from native Santali linguists was shared before production started, not after.
Same-day correction
When QA flagged a scoring issue and a domain mismatch, corrections were applied across all 8 files the same day.
Results
Measured outcomes from this engagement.
5,000+ prompts were evaluated across two rare Indian pairs, with all QA feedback resolved within 48 hours and the delivery accepted through the agreed review path.
| Languages | Santali to Hindi, Oriya to Hindi |
|---|---|
| Volume | 5,000+ prompts evaluated |
| QA resolution | Same-day correction across 8 files |
| Outcome | accepted through the agreed review path |
What supported the result
Why the fit was real
Rare-pair similarity evaluation needs native linguists who also know the methodology, which is exactly the supply that is hard to find for Santali.
What decided the result
Sharing linguist feedback before production and resolving QA the same day is what kept a thin-supply project on track.
What buyers can reuse
- For rare-language evaluation, the bottleneck is trained native linguists, not methodology alone.
- Proactive feedback and same-day QA resolution kept a thin-supply project from stalling.
- The evidence keeps the client details confidential and attributes the metrics only to this engagement.
Continue from this proof
Useful comparisons for the same problem.
Use these links to compare the case with the matching service, buyer guide, and language coverage.
Services and guides
Related services
Languages named
Examples referenced in the engagement.
- Santali
- Oriya
- Hindi
- Rare Indian language pairs
More proof
Related projects
Compare this case with LLM training data coverage and Rare-language evaluation at scale to judge whether the operating pattern fits your brief.
case evidence
Related projects.
These related cases keep the next click close to the same kind of work.
AI data services15,000 categorized retail images delivered ready for object detection and visual search training.
Visual search image data
The challenge. A computer-vision team needed shelf and storefront imagery with enough real-world variance to train models that generalize.
What we did. MoniSa collected across multiple locations, captured lighting and configuration variance deliberately, and organized by category on delivery.
The result. 10,000 supermarket and 5,000 storefront images, structured for direct pipeline ingestion.
Recognise your own project in one of these?
Send the language list and volumeAI data servicesA single voice data project became a recurring relationship, including rare-language work.
Voice data, 500 hours
Problem. A client needed ~500 hours of US English voice data with diversity and privacy requirements, inside a fixed budget.
Action. MoniSa collected inside the client's own app environment with privacy controls, two-layer QC, and terms that protected speaker diversity.
Result. ~500 hours at reviewed quality satisfaction, extended by the client into follow-on rare-language engagements.
AI data services178 annotated hours delivered with 11 of 20 recruits removed before they touched production data.
Bengali pilot, screened
Problem. An AI company needed a Bangladeshi Bengali annotation pilot on a fixed timeline, where dialect and annotation aptitude are separate requirements.
Action. MoniSa over-recruited, screened aptitude separately from fluency, and removed those who did not meet standard before production.
Result. 9 of 20 cleared screening; 178 hours delivered as paid production work with the funnel reported in full.
Buyer questions
Common questions.
What was delivered on this engagement?
Languages: Santali to Hindi, Oriya to Hindi. Volume: 5,000+ prompts evaluated. QA resolution: Same-day correction across 8 files
What control kept the work stable?
Sharing linguist feedback before production and resolving QA the same day is what kept a thin-supply project on track.
Where should similar work go next?
Use AI data services for the delivery model, AI data annotation vendor guide for buyer-side evaluation, and the contact page for a scoped brief.
What happens if you cannot staff one of my language pairs?
For the proposed project, ask for pair-by-pair availability or a recruitment window in writing before agreeing a date. Define qualification and pilot approval for any new contributor before live work. A coverage claim should be checkable before the scope is signed.
Similar brief
Send the constraint behind the metric.
A useful follow-up to a case study names the language mix, review model, deadline, and what proof your buyer team needs before approval.
Send a brief