Case study
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+
verified language specialists
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served since 2015
Measured outcomes
Cross-lingual similarity evaluation
Project overview
What landed, and what made it hard.
A global AI research lab needed cross-lingual semantic textual similarity evaluation for Santali and Oriya paired with Hindi.
Delivery snapshot
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
Why this mattered
Outcome before process.
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
Why the work was difficult, and what MoniSa changed in-flight.
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.
The challenge
The problem to solve
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.
Operating response
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 |
Selection logic
What protected the result.
The selection came down to whether MoniSa could source and review the work at standard, and whether that would hold across the full run.
Why the fit was real
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
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
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.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Santali
- Oriya
- Hindi
- Rare Indian language pairs
More proof
Related proof
Compare this case with LLM training data coverage and Rare-language evaluation at scale to judge whether the operating pattern fits your brief.
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Nearest proof pattern.
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Buyer questions
Answers in writing, before you ask for a call.
The questions buyers send before a scope conversation, answered on the page rather than in a meeting. Take them to your team, then send us the one we did not answer.
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?
You are told before a date is agreed, not after. Coverage is reported pair by pair as staffed today or needing a recruitment window, with the window stated — in writing, while the scope is still being agreed. Nobody new goes onto live work until a pilot batch has been reviewed and signed off. A coverage claim you cannot check before signing is not coverage.
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.
Production-ready brief
01Closest matching challenge from this case02Language pair, dialect, and script coverage03Volume, cadence, or hours to deliver04Reviewer model and acceptance criteria05Security or platform constraints06Proof needed for stakeholder approval