Since 2015
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.
ISO 9001, 27001, and 17100 certified · operator-led multilingual delivery.
Multimodal data operations
One review line means a rare language does not get a different standard from a common one — the same gate, whether the file is Spanish or Sylheti.
Trusted delivery
Certifications, language coverage, and case examples are ready for project review.
Delivered
Built on our own platform
MoniSa DataOps is the operating layer behind 2,000+ AI projects in 300+ languages.
See the platformLanguage coverage
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.
Representative specimens, not the full inventory.
Buyer fit
MoniSa pages route buyers by the risk they are trying to reduce: model quality, client continuity, or release-window certainty.
AI and ML product teams
For teams building ASR, evaluation, safety, search, and LLM systems that need native-speaker judgment, not spreadsheet translation.
Enterprise LSP partners
For LSPs that need controlled capacity for scripts, low-resource languages, and turnaround windows their internal bench cannot safely absorb.
Media and OTT operations
For release teams balancing subtitling, dubbing support, metadata, audio QC, and language review across multiple markets.
What we do not do
Coverage, review, and sourcing are the three places a language programme quietly fails. Here is where MoniSa stands on each, before you ask.
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.
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.
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.
Our own network
Our own verified network, not a rented marketplace.
Meet the networkService architecture
Each service connects the use case, proof fit, specifications, review needs, and buyer questions.
When multilingual model quality depends on difficult language coverage.
Rolling multilingual AI data batches measured against the client-provided benchmark set and acceptance rules.
Open service
When a model needs labeled data in rare, low-resource, or dialect-heavy languages that generic annotation vendors cannot staff with native reviewers.
confidential labeling records show a written annotation guideline, reviewer independence, and inter-annotator agreement (IAA) checks before any batch is scaled, including pairs with extremely limited linguist availability globally.
Open service
When a model needs training examples in rare, low-resource, or indigenous languages, dialects, or scripts that no off-the-shelf dataset covers.
confidential dataset records show structured collection, native-speaker creation, and format validation against the schema the model team supplied, including languages with extremely limited linguist availability globally.
Open service
When a model reads well in English but its outputs in rare, low-resource, and culturally distinct languages need native judgment on accuracy, safety, and tone before release.
confidential evaluation records show a written rubric, independent raters, and senior adjudication on disputed scores across multiple languages, including pairs few vendors can staff with qualified native raters.
Open serviceBy evaluation type: LLM Evaluation, AI Red Teaming.
When the work involves rare pairs, regulated content, mixed scripts, or a launch window that cannot wait for ad hoc sourcing.
confidential rare-language TEP records show parallel language pods, script-specific QA, and senior review without exposing client names.
Open serviceBy document type: Document, Certified, Medical, Legal, Technical.
When the content has to feel native, fit the product, and stay consistent across markets.
1,000+ brands served since 2015 across language and AI data programs.
Open serviceBy content type: Game Localization.
When subtitle timing, dubbing support, metadata, and language review have to land together.
Fixed-window media sprint delivered through timing QC, linguistic review, metadata checks, and client delivery closure.
Open serviceBy deliverable: Subtitling.
When availability, dialect fit, and live risk matter more than a basic language match.
Rare-language healthcare interpretation roster built through staged screening and live-readiness checks.
Open service
When the exchange depends on something visible — a document, a device, an examination, a signed language — and an audio-only line would lose the meaning.
Interpretation delivery records show staged screening, live-readiness review, and named backup coverage before a remote session is confirmed.
Open service
When a conversation begins with no interpreter on site, no notice, and a language the team did not plan for.
Interpretation delivery records show staged screening, dialect confirmation, and named backup coverage before a language is placed on a live call route.
Open service
When care depends on a language the clinical team does not speak, and a family member standing in is not an acceptable control.
A documented medical interpreter deployment shows staged screening, onboarding quality review, and live-readiness checks before interpreters reached clinical sessions.
Open serviceProject flow
A clear view of how your multilingual work is scoped, produced, reviewed, and delivered, so scope, review, and handoff expectations stay clear along the way.
Brief, language pair, file type, and deadline arrive together before the work is routed.
Native-market production runs with glossary, script, format, and reviewer fit visible in the same lane.
Reviewer check, senior sampling, and correction path stay connected before final files leave.
Final files leave with scope context, acceptance notes, and the best follow-up option for the next workstream.
Quality method
MoniSa uses a three-layer system: pre-production gates, in-production controls, and post-delivery review.
Profile review, nativity verification, domain questionnaire, screening call, sample task.
Every assigned team works against the same calibration items before production volume starts.
The first batch is reviewed deeply so instruction drift is caught before scale.
Sampling, senior review, agreement checks, and same-day feedback loops run during production.
Critical errors trigger pause, recalibration, replacement, or operations-lead escalation.
Client feedback feeds back into resource profiles, glossary rules, and the next batch.
case evidence
Each record keeps the useful detail: the challenge, what we did, the quality controls, and the scoped outcome.
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.
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.
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.
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
Scope and coverage from real MoniSa engagements. Each links to its full case study.
Coverage proof
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
Representative of active coverage, not an exhaustive inventory.
Before a language enters production
AI/ML teams
Native-speaker judgment, calibration, and review logic for difficult markets.
Open buyer laneLSP partners
White-label rare-language or deadline-sensitive work with reviewer depth and accountable handoff.
Open buyer laneMedia and OTT
Subtitles, metadata, dubbing support, timing QC, and language review before release windows tighten.
Open buyer laneBuyer questions
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
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.
Specialists from MoniSa's own network of 110,000+ verified language specialists — 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.
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.
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.
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.
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 projects since 2015.
Next step
A useful brief names the language, content, deadline, review depth, and proof the buying team needs.
Production-ready brief
01Language pair, dialect, and script02Content or data type03Volume and deadline04QA and reviewer requirement05Security and access requirement06Proof needed for buyer approvalCapability at a glance
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.