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 in 140+ languages.

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

140+AI project languages

ISO 9001:2015 · 27001:2022company certifications

Four modalities

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.

Text and NER

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.

Image and computer vision

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.

Audio and speech

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.

Governance

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.

Controls to agree and verify

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.

Language expertise

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 includes 110+ rare, indigenous and low-resource languages; AI data services projects have been delivered in 140+ languages. 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.

Platform + people

Connect the workflow to a MoniSa-managed specialist network

The company-wide specialist network spans 300+ languages; AI data services projects have been delivered in 140+ 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

Services

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.

Start with the project details

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

01 Modality and source format 02 Languages and volume 03 Schema and acceptance rules 04 Security and delivery window

Buyer questions

Common 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. Separately, MoniSa has delivered AI data services projects in 140+ languages.

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 110+ 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 in 140+ languages; each new project is confirmed for its exact language and reviewer fit.

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.