Screen
Profile review, nativity verification, domain questionnaire, screening call, sample task.
AI Training Data Services for work where a model needs training examples in rare, low-resource, or indigenous languages, dialects, or scripts that no off-the-shelf dataset covers.
Building, collecting, and structuring multilingual training datasets across speech, text, image, and audio, specializing in rare and indigenous languages where no clean, rights-cleared dataset exists yet.
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.
Service details
AI Training Data Services 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.- Typical inputs
- Data spec, target languages and dialects, schema, format rules, seed prompts or scenarios, consent and licensing requirements
- Controls
- Source vetting, native-speaker creation, schema and format validation, deduplication, sampling review
- Best fit
- AI training data services, multilingual training data, speech and audio collection, text dataset creation, low-resource language coverage
AI Training Data Services
When to use it.
When a model needs training examples in rare, low-resource, or indigenous languages, dialects, or scripts that no off-the-shelf dataset covers.
Formats we handle
AudioSpeech and voiceover
TextDocuments, UI, copy
ImageStills and scans
MetadataTags and taxonomy
Platform-backed training data
Scope text, image and audio collection with checks.
MoniSa reports 185,000+ hours of speech data across delivered programs. Define collection and annotation checks for the new project.
See the training-data platformSpecification
Project requirements and deliverables.
What we need from you, how the work is reviewed, and what you receive.
| Typical inputs | Data spec, target languages and dialects, schema, format rules, seed prompts or scenarios, consent and licensing requirements |
|---|---|
| Review path | Source vetting, native-speaker creation, schema and format validation, deduplication, sampling review |
| Strongest fit | AI training data services, multilingual training data, speech and audio collection, text dataset creation, low-resource language coverage |
| How the work runs | Spec and schema lock, sample set for sign-off, then structured dataset delivery in scheduled drops |
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.
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
Proof that matches AI training data services, not generic language work.
Explore the project records for scope, review method and delivered results.
AI data servicesA 150-specialist on-site deployment in Tokyo for direct model-refinement work.
On-site linguist deployment
The challenge. An AI company needed Japanese-language specialists on-site, working directly with its engineering team.
What we did. MoniSa deployed 150 specialists in Tokyo and settled into a steady on-site production team.
The result. On-site collaboration moved model refinement faster than a remote workflow.
Recognise your own project in one of these?
Send the language list and volumeMultimedia services50 songs transliterated and synchronized across 9 Indian languages.
Song transliteration and sync
Problem. A global short-video platform needed song lyrics readable and singable for non-native-script audiences.
Action. MoniSa transliterated each song for faithful sound mapping and synchronized the text to the music.
Result. 50 songs made followable across 9 Indian languages, line by line in time with the audio.
Localization servicesA packaged-foods brand entered MENA markets with content ready for launch.
MENA market-entry localization
Problem. A packaged-foods brand needed labeling, marketing, and voiceover localized across three languages before a MENA launch.
Action. MoniSa ran labeling, brochures, marketing, and voiceover as one coordinated program against the launch window.
Result. 150,000 words and 40 hours of voiceover localized across Arabic, Kannada, and Malayalam for market entry.
Translation servicesAlmost 150,000 words of Arabic delivered in a 20-day batched sprint.
Arabic content sprint
Problem. A global ride-hailing platform needed 147,916 words of Arabic translated inside 20 days.
Action. MoniSa sourced for throughput and shipped in reviewed batches across the 20-day window.
Result. 147,916 words delivered across a 20-day window in reviewed batches, landing in stages rather than one final hand-off.
Continue the training-data plan
Link dataset readiness to the review work that follows.
Training data is useful only when collection, annotation, and evaluation decisions stay connected.
Data source and rights review
Check provenance, consent and schema on a pilot source set before scaling collection.
AI data program scope
Plan the languages, data and review requirements for your program.
Multilingual data annotation
Define the labeling and reviewer controls for the dataset.
Prompt and model evaluation
Plan the human-review path for model outputs after training.
LLM evaluation services
Score the trained model output against a rubric, per language, once the dataset has done its work.
AI red teaming
Find the failures a guardrail dataset then has to close, in every language the model answers in.
AI/ML program lane
See the buyer path from data requirements to production evidence.
Company-wide language coverage
Availability and specialist fit are confirmed for each project.
110,000+
native linguists and AI data contributors
· Founder-reported combined network · 4 Oct 2026
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare, indigenous and low-resource languages
1,000+
organizations served since 2015
Buyer questions
Common questions.
What are AI training data services?
AI training data services build and curate the example data a model learns from: collecting speech and audio, creating or sourcing text, gathering images, and structuring it all to a defined schema. MoniSa focuses on multilingual and low-resource coverage, where ready-made datasets usually do not exist.
How does MoniSa build a multilingual training dataset?
The work starts from a data spec and a target schema. MoniSa vets sources, uses native speakers to collect or create the data, validates format and structure, removes duplicates, and ships a sample set for sign-off before scaling. Consent and licensing requirements are confirmed up front.
What is the difference between building training data and annotating it?
Building training data means producing or collecting the raw examples and structuring them: speech recordings, written text, images, scenario sets. Annotation means adding labels to data that already exists. MoniSa offers both as separate, scoped services so a model team can use either or both.
Can MoniSa create training data for low-resource or rare languages?
Yes. MoniSa has delivered AI data services projects in 140+ languages. For rare or low-resource pairs, MoniSa confirms native-speaker availability, dialect and script fit, and the collection or creation method before committing to a dataset build.
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
Tell us about your project.
Share the languages, content, volume and deadline. Include any review or security requirements.
Send a brief