Build multilingual AI, add specialist language capacity, or prepare media for new audiences. Explore the approach that fits your team.
Who we work with
AI and ML product teams
For teams building speech recognition, search and language models.
Native speakers label training data and assess model responses for accuracy and safety.
Enterprise LSP partners
Translation, editing and proofreading for rare languages and regional scripts.
Teams work to your terminology and review requirements, including white-label projects.
Media and OTT operations
For teams delivering subtitling, dubbing and language review across markets.
Work covers timing, audio quality and metadata, with the required delivery formats.
case evidence
See what each project involved, what we delivered and the results.
The challenge. A technology buyer needed translation, editing and proofreading across rare languages and scripts on a tight schedule.
What we did. MoniSa organized the work by language and script, completed parallel batches and included senior review.
The result. The buyer received reviewed translations with a clear route for questions and corrections.
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Problem. A technology company needed evaluation work in languages where qualified translator pools can be extremely small.
Action. MoniSa assigned separate evaluation reviewers, built contingency backup per language, and tracked delivery by language cluster.
Result. The evaluation set moved through controlled delivery with language-specific backup coverage.
Problem. An AI company needed transcription, labeling, and segmentation across languages with limited existing resource pools.
Action. MoniSa combined in-country sourcing, peer review, senior review, and rolling monthly batches.
Result. The client received multilingual audio data batches measured against its own benchmark set and acceptance notes.
People and platform
Training data, AI-output review, taxonomy and terminology work, with specialist teams and review records suited to the project.
Explore the platformMoniSa maintains its own network. Language, domain, task and availability are confirmed for the proposed team before work starts.
See the specialist networkAvailability and specialist fit are confirmed for each project.
Buyer questions
Pick by the risk you are managing: AI/ML teams need language coverage and reviewer calibration, LSP partners need white-label overflow capacity, and media and OTT teams need time-coded delivery and market review. Each lane page maps that buyer in detail.
Yes. Programs often combine lanes — for example AI data work alongside media localization — under one accountable team, so coordination and quality stay consistent across the whole scope.
Each lane leads with proof matched to that buyer: rolling AI data and evaluation work, rare-language overflow handled through parallel language pods, or fixed-window media sprints with timing and metadata QC.
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.
Next step
Share the languages, content, volume and deadline. Include any review or security requirements.
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