The platform behind the work

The data platform behind 2,000+ AI data services projects, in 300+ languages

MoniSa DataOps is the operating layer behind thousands of AI projects across 300+ languages and 4,500+ dialects. Text, image, audio, video — collected, annotated, and human-reviewed on one system, with the reviewer calibration and schema governance that hold quality steady on the rare languages that break everyone else's pipeline.

Start a project See how delivery maps

Bring us the hardest languages you have. That's the part we're known for.

One governed system Collect. Annotate. Review. Govern.

Four modalities move through one calibrated review and audit layer.

Coverage: 300+ languages
Verified specialists: 110,000+
Governance: Triple ISO certified

300+languages

4,500+dialects

11 yearsof continuous delivery

2,000+AI projects

1,000+brands served

ISO 9001 / 27001 / 17100certified

Four modalities

One review standard across every data type.

Each workspace is shaped for the modality. Schema control, human review, and the audit trail stay connected.

Text and NER

Every label earns its place before your model ever sees it

Entity tagging, classification, and multilingual text annotation run through a review queue where a specialist accepts, reworks, or escalates each submission against a defined error taxonomy. AI moves fast where speed helps; a human makes the call where being wrong is expensive. We have delivered 140,000+ samples, 95,000+ documents of OCR and document AI, and 220,000+ prompts of multimodal and LLM data. The result is data you can train on without flinching.

Image and computer vision

Bounding boxes checked box by box, against the label schema, at production volume

Object detection, regions, and attributes get reviewed against the label schema with per-object precision. Missing labels, boundary errors, and guideline violations surface in review, long before they show up as your model's error rate. We have collected 130,000+ images and 95,000+ hours equivalent of video without letting the standard slip.

Audio and speech

Speech in the languages your last vendor said they "supported"

Diarization, transcription, and environment tagging across languages, reviewed segment by segment with a waveform and timestamps. Accents, code-switching, and background noise get handled by people who actually speak the language. We have collected 85,000+ hours of speech across 140+ languages and 90,000+ hours of audio annotation, with 15,000+ hours of transcription across 60+ rare languages — the audio work most pipelines quietly outsource and hope nobody checks.

Governance

Governance that holds when you scale from one language to a hundred

Every project runs on a versioned label schema — audio transcription, legal NER, object detection, content safety — with workflow templates, calibration, drift detection, and a full audit trail. When volume jumps or a new language lands, the standard holds instead of drifting. This is the discipline behind our ISO 9001 and ISO 27001 certifications, and ISO 17100, which covers translation services — not a slide about them.

How the standard is enforced

Quality you can audit, not quality we assert

Any vendor can promise a review standard. These are the mechanisms that make ours checkable, and the reports they produce are yours on request.

Calibration before production, not after complaints

Every reviewer is scored against gold tasks before they touch your data, and again in calibration rounds while the work runs. When someone starts drifting from the standard, the drift shows up as an alert against their scorecard rather than as a batch you have to reject.

Errors are classified, not described

A reviewer rejecting an item picks from a defined error taxonomy rather than typing a comment. That is what makes a quality report countable: you get the distribution of what went wrong by type and by project, instead of a rate with no anatomy.

Every change is on the record

Each mutation writes an audit entry carrying who did it, the role they held, the item, the action, and the state before and after. Nothing in the pipeline can be quietly changed later, which is the question a procurement or security review asks first.

Deadlines are enforced by the queue itself

Work is handed out in SLA-deadline order and locked to one person at a time, so two annotators cannot take the same item and an urgent job cannot sit behind a routine one. On-time rate, breaches, and the tasks currently at risk are all measured.

Disagreement has a defined path

A rater who believes a review decision is wrong files a dispute, and a lead resolves it. The disagreement is recorded and adjudicated rather than absorbed silently into the quality figure, which is how a suspiciously clean acceptance rate usually happens.

Retention is a project setting

How long your data is held, and what happens to it afterwards, is configured per project against a retention policy rather than left to whoever cleans up. Pre-release model output and regulated source material are handled under the rules agreed before intake.

The operating moat

Rare languages are not the exception here. They are our default setting

110,000+ verified language specialists. 110+ rare and indigenous language pairs. 140+ languages set up for AI-data work. This is the bench most teams cannot build internally and few suppliers can match — and it is why the projects other people turn down land on this platform. Human-in-the-loop review at this scale, in these languages, with this governance, is the hard thing. We built the platform so it runs like the easy thing.

The same platform behind a 131-language LLM data run — 110 of them rare or indigenous — and a 54-language-pair LLM safety and quality evaluation with inter-annotator agreement held across every pair.

Platform + people

The platform runs on a network we own

MoniSa DataOps is the software. The specialist network is who runs it — 110,000+ verified language specialists — linguists and annotators — across 300+ languages, sourced and vetted by us, not rented from a marketplace.

Source

Recruited from certified professional registries and specialist communities worldwide.

Verify

Credentials, language pairs, and domain experience are checked before anyone enters the pool.

De-duplicate

One real, unique, reachable record per specialist. No inflated headcounts.

Match

The right specialist for your language, domain, and timeline.

See the network

Delivery map

One platform behind five ways we deliver

Buy the outcome you need. The same governed operating layer sits behind it.

Human review of AI outputs

Flag unsafe, biased, or wrong model responses, with the audit trail to prove it.

Building training data sets

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

Content tagging and taxonomy

Descriptors and metadata that stay consistent across a whole catalog.

Terminology governance

Keep language assets current as products and guidelines change.

Managed reviewer teams

Native-speaker reviewers with the calibration and surge capacity you cannot staff in-house.

Hard scope welcome

Bring us the languages and the volume that made your last vendor go quiet

Tell us the modality, the languages, and the scale. We'll run a batch through the platform and show you the review trail behind it — the part that decides whether your model ships or slips.

Start a project

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

Buyer 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.

What is MoniSa DataOps?

MoniSa DataOps is MoniSa's annotation, review, and quality platform for building AI training data across text, image, audio, and video, in 300+ languages.

What makes it different?

110,000+ verified language specialists, 110+ rare and indigenous language pairs, human review with senior sampling and agreement checks on every project, and ISO-certified governance — built for the languages and volume few suppliers can cover.

How does MoniSa keep quality consistent?

Versioned label schemas, reviewer calibration, gold tasks, and drift detection, backed by a full audit trail.

Which languages does MoniSa cover?

300+ languages and 4,500+ dialects, including the rare and indigenous languages that most pipelines can't staff.

What happens if you cannot staff one of my language pairs?

You are told before a date is agreed, not after. Coverage is reported pair by pair as staffed today or needing a recruitment window, with the window stated — in writing, while the scope is still being agreed. Nobody new goes onto live work until a pilot batch has been reviewed and signed off. A coverage claim you cannot check before signing is not coverage.