Language expertise for multilingual AI.
For teams building speech recognition, search and language models.
Native speakers label training data and assess model responses for accuracy and safety.
Explore AI servicesLanguage expertise for AI and global content
AI data, translation, media and interpreting, with specialists for the languages your project needs.
300+ company-wide languages
110,000+
Native linguists and AI data contributors
Founder-reported combined network · 4 Oct 2026
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Across scripts
Annotation, translation and media in the scripts your audience uses.
6,971
Language-pair combinations
213
Countries and territories
4,500+
Dialects and variants
ISO 9001:2015ISO 27001:2022ISO 17100:2015 · translation
Clear scope. Human-reviewed delivery.
Language & AI data
See the work
take shape
Follow a piece of text, a recording, an image or a video through the work.
A sentence. Three names to find.
MayaPerson meets LeoPerson in DelhiPlace.
People and places, clearly identified.
- The source
- Plain text, with meaning in every word.
- The work
- A specialist identifies each name and checks its context.
- The result
- Consistent labels, ready for your dataset.
Two voices. A readable conversation.
00:01Speaker 1
Shall we start here?
00:04Speaker 2
Yes. I’m ready.
Every turn has a speaker and a time.
- The source
- A recording with different voices.
- The work
- Speech becomes text; speaker changes are marked.
- The result
- A transcript aligned to the recording.
Two objects. Two precise boundaries.
Person · boundary tracedMonitor · boundary traced
An image becomes labeled training material.
- The source
- An image with objects to identify.
- The work
- Specialists trace boundaries and check each label.
- The result
- Annotations that describe what the image contains.
Words that arrive with the moment.
Let’s take a closer look.
The subtitle follows the picture, without rushing the reader.
- The source
- A video and the words being spoken.
- The work
- Subtitles are translated, timed and checked on screen.
- The result
- Readable subtitles, ready to play with the picture.
Multilingual work for AI, media, and language-service teams that need review before launch
Certifications, language coverage, and case examples are ready for project review.
Delivered
- 1,000+
- organizations served since 2015
- 2,000+
- AI projects delivered
- 11 years
- of continuous delivery
- 62
- documented case studies
AI / ML teamsTraining data and human review for multilingual model workEnterprise LSPsWhite-label rare-language and overflow capacityMedia / OTTSubtitling, dubbing, voiceover, and catalog localizationISO 9001Quality managementISO 27001Information securityISO 17100Translation services
Scope annotation, human review and quality together
Use a pilot to set annotation rules, review depth and quality checks for the proposed project.
See the platformLanguage, script and local knowledge
Work with native reviewers across 300+ company-wide languages and 4,500+ dialects, including rare and low-resource work.
Writing systems
DevanagariAbugida · Hindi, Marathi, Nepali
Review considers the script, reading direction and local usage.
Language work for AI, translation and media teams
Explore the work for AI teams, language-service partners and media companies.
Rare-language support for language-service partners
Translation, editing and proofreading for rare languages and regional scripts.
Teams work to your terminology and review requirements, including white-label projects.
Explore LSP support
Subtitles prepared for release
For teams delivering subtitling, dubbing and language review across markets.
Work covers timing, audio quality and metadata, with the required delivery formats.
Explore media servicesWhat you can expect
How we select specialists, review the work and agree the requirements for your project.
Specialists selected for the assignment
MoniSa maintains its own network. The proposed project team is checked for language, domain, task and availability before assignment.
Human review of machine translation
Where machine translation is permitted, a human reviewer checks the draft against the source under our ISO 17100-certified translation process.
Availability before scheduling
Send the language pairs before agreeing the contract. We confirm availability and any recruitment window before setting the launch date.
Coordinated service delivery
Translation, localization, multimedia, interpretation and AI data work can be coordinated under one contract and one point of contact. The project scope sets terminology ownership, review responsibilities and handoffs.
Work in your existing systems
We work with your existing CAT tools, file formats and access controls. We confirm the working environment during scoping.
Coverage hours agreed for the programme
Production handoffs can span time zones. The scope records coverage hours for the programme against its volume and timeline before you plan a release.
110,000+ native linguists and AI data contributors · Founder-reported combined network · 4 Oct 2026
Company-wide language coverage spans 300+ languages. Proposed specialists are checked for each project.
Meet the networkWhat do you need help with?
Find the work you need, then explore how we approach it.
AI data
Collect, label and review the text, speech, images and video your models need.
Translation
Translate documents with attention to meaning, terminology and the format people use.
Localization
Adapt products and content to the language, culture and space of each market.
Interpreting
Connect people in live conversations, in person, by phone or over video.
Multimedia
Bring video and audio to new audiences through subtitles, captions and voices.
From your brief to the finished work
Agree the details before work starts. Stay involved where your input matters. Receive files prepared for their intended use.
Tell us about the project
Share the languages, source files and deadline. We agree what the finished work needs to include.
Work with the right specialists
Linguists use the agreed terminology, style and file formats for the audience you need to reach.
Review and refine
The work is checked, corrections are made, and outstanding questions are resolved before delivery.
Receive the finished work
You receive the agreed files and review notes, with a clear point of contact for feedback.
Quality starts before the first batch moves
MoniSa uses a three-layer system: pre-production gates, in-production controls, and post-delivery review.
Screen
Profile review, nativity verification, domain questionnaire, screening call, sample task.
Calibrate
Every assigned team works against the same calibration items before production volume starts.
Pilot
The first batch is reviewed deeply so instruction drift is caught before scale.
Review
Sampling, senior review, agreement checks, and same-day feedback loops run during production.
Escalate
Critical errors trigger pause, recalibration, replacement, or operations-lead escalation.
Learn
Client feedback feeds back into resource profiles, glossary rules, and the next batch.
Projects relevant to your work
See what each project involved, what we delivered and the results.
Translation and LSP support257,000 words across 8 rare languages and 4 scripts in 10 days.
Rare-language TEP surge
The challenge. A technology buyer needed translation, editing and proofreading for a fixed product launch.
What we did. MoniSa delivered rolling batches with script-specific checks, independent editing and final proofreading.
The result. The client received reviewed batches every two days, with sample review and controlled corrections.
Discuss a similar project
Start a projectRare-language evaluation set
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.
AI audio data pipeline
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.
Prompt safety evaluation
Problem. AI platforms needed language-aware safety evaluation across many pairs where cultural harm and bias do not read the same way.
Action. MoniSa deployed evaluator cohorts, calibration sets, and drift checks across rolling rating batches.
Result. The client received multilingual safety data that engineering teams could use to refine model behavior.
Media and metadataFixed-window OTT rare-language sprint.
OTT rare-language sprint
Problem. A streaming team needed subtitle, dubbing, and metadata work to land for a fixed release window.
Action. MoniSa handled subtitles, dubbing support and metadata in parallel, checking timing and language before client handoff.
Result. The release package moved through timing, language, and metadata checks before client review.
A closer look at three projects
Scope and coverage from real MoniSa engagements. Each links to its full case study.
Language expertise across borders
300+ languages and 4,500+ dialects, with specialists matched to the language and subject of your project.
Languages and dialects
- 300+languages across active service lines
- 4,500+dialects and regional variants
- Rareindigenous & low-resource work
ISO 9001:2015ISO 27001:2022ISO 17100:2015 · translation
Confirm the language, reviewers and type of work
- Script and dialect fitConfirm market, script direction, dialect, register, and reviewer profile before production.
- Reviewer availabilitySeparate production, senior review, backup coverage, and escalation before the timeline locks.
- Work-type matchMatch the path to model evaluation, translation, media release, interpretation, or localization.
Language specialists for AI review
Native speakers review AI outputs using agreed criteria, with sample reviews to align their assessments.
Explore AI data servicesLanguage support under your brand
White-label support for rare-language projects and tight deadlines, with specialist review and clear delivery responsibilities.
Explore LSP partnershipsLaunch support beyond translated words
Subtitles, metadata, dubbing support, timing QC, and language review before release windows tighten.
Explore media servicesHome questions
How is language-pair availability confirmed?
Ask for availability for each language pair before agreeing the schedule. MoniSa confirms which pairs are staffed and which require recruitment in the project brief.
Who carries out the work?
MoniSa draws on a combined network of 110,000+ native linguists and AI data contributors · Founder-reported combined network · 4 Oct 2026. The proposed team is checked for language, domain, task and availability before assignment. Machine translation is treated as a first draft: a named human reviewer checks it against the source under MoniSa's ISO 17100-certified translation process before it reaches the buyer.
What does each MoniSa certification cover?
Three independent certifications: ISO 9001:2015 for quality management, ISO 27001:2022 for information security, and ISO 17100 for translation services. ISO 17100 is scoped to translation specifically — it is not a blanket mark over every service line, and any vendor implying otherwise is overstating it.
What proof should I ask for before signing with a language vendor?
Documented work that matches your content type, language difficulty, and outcome — not aggregate volume. Total words delivered says nothing about whether a vendor has handled your problem. MoniSa publishes 62 documented case studies, each stating the scope, the constraint that made it hard, and the result.
What do you need from me to scope a project accurately?
Four things: the language pairs, the content type, the volume and deadline, and the acceptance criteria the output will be judged against. Those determine feasibility. Anything missing has to be assumed instead, and wrong assumptions are the usual reason an estimate and an invoice disagree.
How is AI data work different from translation work?
Translation carries meaning across languages against a source text, so the source defines what is correct. AI data work — collection, annotation, evaluation, review — produces or judges material against a task specification, so the acceptance criteria define what is correct and there is often no source to check against. The staffing, the QA design, and the failure modes all differ. MoniSa runs both and has delivered 2,000+ AI data services projects since 2015.
What happens if you cannot staff one of my language pairs?
We confirm availability or the recruitment window for each requested pair before agreeing the date. New contributors must meet the project qualification and pilot requirements before production.
Tell us about your project
Include your language pairs, content type, volume, deadline and review requirements.
Send a brief