AI and ML product teams
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 servicesSince 2015
Launch AI and multilingual content with rare-language specialists.
Your launch date should not be set by your hardest language. We run AI training data, translation, subtitles and interpretation for AI, media and localization teams — staffed from a specialist network built by us, not rented from a marketplace.
Company ISO 9001:2015 and 27001:2022 certified; ISO 17100:2015 applies to translation.
Since 2015300+ company-wide languages
110,000+
Verified language specialists
· Counted from our linguist database · verified June 2026
110+
Rare & indigenous
language pairs
language pairs
Availability is confirmed for each language pair before scheduling.
अ
ع
Across scripts
From annotation to dubbing, in the script the market actually reads.
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.Illustrative examples.
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.
Find the objects. Follow their edges.
Vehicle
Tree
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.
Trusted delivery
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+
- brands 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
Built on our own platform
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 coverage
Native review across the writing systems your users actually read.
Coverage means the right reviewer for each script and direction — 300+ languages, 4,500+ dialects, and 110+ rare and indigenous language pairs, sourced as native-reviewer pods.
One review line · every script
DevanagariAbugida · Hindi, Marathi, Nepali
Script, direction, and locale are one decision here, not three handoffs.
Representative specimens, not the full inventory.
Who we work with
Language work for AI, translation and media teams.
Explore the work for AI teams, language-service partners and media companies.
Enterprise LSP partners
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
Media and OTT operations
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 we do not do
The answers buyers rarely get in writing.
Sourcing, review, coverage, scope, integration and turnaround are where a language programme quietly fails. Here is where MoniSa stands on each, before you ask.
No rented marketplaces.
Our network, our verification. Every specialist is sourced, vetted and de-duplicated by us before they touch your file — not rated by strangers after the fact.
No unreviewed machine output.
Machine translation is a first draft here. A named human reviewer checks it against the source under our ISO 17100-certified translation process before it reaches you.
No coverage you cannot check first.
Send the language list before the contract. We tell you which pairs we staff today and which need a recruitment window — in writing, before you commit a launch date.
No vendor juggling.
Translation, localization, multimedia, interpretation and AI-data work run under one contract, one process and one point of contact. Splitting them across suppliers is where handoffs drop, terminology drifts between vendors, and a timeline quietly becomes the slowest one.
No rip-and-replace.
We work inside the CAT tools, file formats and access controls you already run. Starting with us does not mean migrating a system, retraining your team, or putting another tool through your security review.
No single-clock bottleneck.
Production is handed off across time zones rather than run from one office clock, so an overnight turnaround does not sit waiting for one team to arrive. Coverage hours are set per programme against your volume and timeline, and written into the scope before you plan a release.
Our own network
110,000+ verified language specialists · Counted from our linguist database · verified June 2026 across 300+ languages.
Our own verified network, not a rented marketplace.
Meet the networkOur services
What 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.
Working together
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 method
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.
case evidence
Projects relevant to your work.
Each record keeps the useful detail: the challenge, what we did, the quality controls, and the scoped outcome.
Translation and LSP supportRare-language TEP surge across multiple languages and scripts.
Rare-language TEP surge
The challenge. A global technology buyer needed rare-language translation, editing, and proofreading at a speed that a normal vendor bench could not absorb.
What we did. MoniSa activated language pods, separated script-specific QA, and staged production in parallel batches with senior review.
The result. The buyer received sprint-speed rare-language capacity with project-scoped quality review and a controlled correction lane.
Recognise your own project in one of these?
Send the language list and volumeAI evaluationRare-language evaluation set for a constrained AI program.
Rare-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 data servicesRolling multilingual audio data pipeline across rare-language pools.
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.
AI evaluationGenAI prompt safety review across multilingual rating lanes.
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 ran parallel language pods with timing QC, linguistic review, and metadata checks before client handoff.
Result. The release package moved through timing, language, and metadata checks before client review.
Delivered engagements
A closer look at three projects.
Scope and coverage from real MoniSa engagements. Each links to its full case study.
Coverage proof
Coverage should make the buyer's risk clearer.
MoniSa's 300+ language and 4,500+ dialect footprint is useful because it is tied to sourcing, reviewer fit, QA method, and the work type being bought.
Coverage as native-reviewer depth
- 300+languages across active service lines
- 4,500+dialects and regional variants
- 110+rare & indigenous language pairs
Representative of active coverage, not an exhaustive inventory.
ISO 9001:2015ISO 27001:2022ISO 17100:2015 · translation
Before a language enters production
Three checks make coverage usable.
- 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.
AI/ML teams
Coverage protects model quality.
Native-speaker judgment, calibration, and review logic for difficult markets.
Explore AI data servicesLSP partners
Overflow keeps the client relationship clean.
White-label rare-language or deadline-sensitive work with reviewer depth and accountable handoff.
Explore LSP partnershipsMedia and OTT
Launch support beyond translated words.
Subtitles, metadata, dubbing support, timing QC, and language review before release windows tighten.
Explore media servicesBuyer 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.
How do I check a vendor can really cover my languages before I commit?
Ask for the coverage position in writing before the contract, pair by pair. MoniSa covers 300+ languages and 4,500+ dialects and will say which pairs are staffed today and which need a recruitment window — in writing, before a launch date is agreed. A coverage claim you cannot check before signing is not coverage.
Who actually does the work — a marketplace, employees, or machines?
Specialists from MoniSa's own network of 110,000+ verified language specialists · Counted from our linguist database · verified June 2026 — linguists, annotators, and reviewers — sourced, vetted and de-duplicated in-house rather than rated by strangers on a marketplace. 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 is MoniSa certified for, and what does each certification actually 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?
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
Send the details that decide the quote.
A useful brief names the language, content, deadline, review depth, and proof the buying team needs.
Send a brief