The platform behind the work

The data platform behind 2,000+ AI 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, at volume that makes other vendors flinch

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, 27001, and 17100 certifications, not a slide about them.

The operating moat

The languages that scare other vendors 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 most vendors cannot fake -- 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

Ask the questions weak vendors avoid.

Short answers for buyers checking fit, coverage, quality method, and next-step readiness.

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 on every submission, and ISO-certified governance -- built for the languages and volume most vendors can't 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.

Capability at a glance

The answers most briefs open by asking for.

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.

Languages and locales: 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
Specialist network: 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
Capacity and mobilisation: Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
Sourcing constraints: Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
Deliverables and specs: Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
Comparable work: 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
Certifications: ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
Commercial basis: Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.

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.