Human expertise for multilingual AI data

Text, images, speech and video, prepared and reviewed by language specialists. MoniSa has delivered 2,000+ AI data services projects.

Start a project Explore the work

Tell us the language and the work you need.

A sentence becomes labeled data

Source text

MayaPerson meets LeoPerson in DelhiPlace.

The specialist’s decision

Maya and Leo name people. Delhi names a place.

The resulting labels

Maya: Person
Leo: Person
Delhi: Place

2,000+AI data services projects

ISO 9001:2015 · 27001:2022company certifications

Set review and acceptance rules for each data type

For each modality, agree schema controls, human review and the review record before production begins.

What can a specialist label?

Names and places: Identify named entities in a sentence.
Topics: Assign a category to a passage.
Meaning and tone: Review text in its language and context.

Agree the label definitions and examples before production.

Decide which labels pass before training

For a text pilot, agree the label taxonomy, sampling depth, rework and escalation rules, and acceptance criteria. Inspect how a specialist applies them before volume. MoniSa has delivered 140,000+ samples, 95,000+ documents of OCR and document AI, and 220,000+ prompts of multimodal and LLM data.

Where does the person end?

Illustrative work scene with a person seated beside a monitor

PersonMonitor

Follow the visible outline. Keep the person and the monitor as separate regions.

Test object and region labels before scaling the batch

Set object-level checks, sampling depth and correction rules on a representative image batch. Inspect missed objects, boundaries and guideline disagreements before volume. MoniSa has collected 130,000+ images and 95,000+ hours equivalent of video.

A new voice. A new turn

Timing animation · no audio playback

Speaker 1Speaker 2

00:01
Speaker 1
Shall we start here?
00:04
Speaker 2
Yes. I’m ready.

Mark the speaker change, then align each turn with its words.

Review speech in context

For an audio pilot, agree segment review, timestamp rules and reviewer fit by language. Test accents, code-switching and noise on representative recordings. MoniSa reports 185,000+ hours of speech data and 175,000+ hours of audio data. Other records include 90,000+ hours of audio annotation and 15,000+ hours of transcription across 80+ languages; these scopes may overlap and are not added.

The sentence changes the label

Labeling rule

Use Person for a person’s name. Use Place for a geographic name.

Jordan is meeting us in Paris.

Jordan Person

Here, Jordan is someone who can meet us.

Paris Place

Paris tells us where the meeting takes place.

A shared rule needs examples—and a reason for each decision.

Agree on the rules before work begins

Agree on labeling rules, sample reviews, quality checks and how questions will be resolved before production. MoniSa holds ISO 9001:2015 and ISO 27001:2022 certifications; ISO 17100:2015 applies specifically to translation services.

Make the review plan testable on a pilot

Use a representative pilot to decide the controls, reporting and acceptance evidence the project needs before volume.

Quality decisions

Agree calibration before production

Agree gold examples and a reviewer-calibration method on the pilot. Specify how disagreement or drift will be checked for this project.

Define useful error categories

Set the error taxonomy and rejection reasons in the brief. Ask the pilot to show whether those categories produce findings the buyer can act on.

Delivery records

Choose the actions to record

Confirm which reviewer actions and revisions the project will record, and request a sample report before intake.

Set priority and assignment rules

Agree task assignment, deadline priority and escalation rules for the scoped workflow. Inspect a pilot task record before production volume.

Accountability

Agree a dispute path

Name who will review a disputed decision, what evidence they need and how the outcome should be returned in the project report.

Set retention before transfer

Set retention and closeout requirements in writing before data transfer, then confirm the controls available for this workflow.

Rare-language work needs a language-by-language fit check

MoniSa reports a combined network of 110,000+ native linguists and AI data contributors (Founder-reported combined network · 4 Oct 2026). Company-wide coverage spans 300+ languages, including rare-language work. For a new scope, verify current reviewer fit, pilot results and governance by language before making a coverage commitment.

MoniSa has documented a 131-language LLM-data engagement, including 110 rare or indigenous languages, and a separate 54-language-pair LLM safety and quality evaluation. Inspect each case record for its review method and result.

Connect the workflow to a MoniSa-managed specialist network

The company-wide specialist network spans 300+ languages. AI data services projects have been delivered across multiple languages. For a proposed project, confirm the current language, task and reviewer fit before assignment.

Source

Identify candidates for the language, domain and task under review.

Verify

Confirm relevant credentials, language pairs and experience for the proposed team.

De-duplicate

Check duplicate records and reachability before relying on a proposed roster.

Match

Confirm reviewer fit and availability before assignment.

See the network

What do you need help with?

Choose the work type, then agree the review workflow, decision record and acceptance evidence for that project.

Human review of AI outputs

Review model responses against agreed policy categories and specify the decision record in the pilot.

Building training data sets

Collect and annotate text, image, audio, and video at volume.

Content tagging and taxonomy

Scope descriptors, metadata rules and consistency checks on a representative catalog batch.

Terminology governance

Keep language assets current as products and guidelines change.

Managed reviewer teams

Check language availability, reviewer fit, calibration and surge capacity for the proposed schedule.

Bring the languages and volume that are difficult to staff

Describe the modality, languages, intended use and scale. We will scope a pilot and agree what review evidence can be inspected before data transfer.

Start a project

Platform questions

What is MoniSa DataOps?

MoniSa DataOps supports annotation and review planning for multilingual AI data work. Text, image, audio and video requirements are scoped by project, including exact language and reviewer fit.

What makes it different?

MoniSa reports a combined network of 110,000+ native linguists and AI data contributors (Founder-reported combined network · 4 Oct 2026). Company-wide coverage includes rare, indigenous and low-resource languages. For a scoped job, agree reviewer fit, sampling and agreement checks on a pilot. The company holds ISO 9001:2015 and ISO 27001:2022 certifications.

How does MoniSa keep quality consistent?

For a scoped programme, agree the label schema, reviewer calibration, sampling and disagreement checks, and which decision records the workflow will retain. Inspect a pilot report before volume.

Which languages does MoniSa cover?

Across all company service lines, MoniSa covers 300+ languages and 4,500+ dialects. AI data services projects have been delivered across multiple languages; each new project is confirmed for its exact language and reviewer fit.