AI data labeling and training
AI data labeling by people who understand the language
Your model is only as good as its training data. We deliver human-annotated, multilingual datasets for LLMs, computer vision, and speech, labeled by native-language experts who catch the nuance, intent, and cultural subtext that automated labeling misses.
La reunión con Banco Andino en Lima se movió al jueves, y el cliente está molesto.
“The meeting with Banco Andino in Lima moved to Thursday, and the client is upset.”
- intent: reschedule
- sentiment: negative
- annotator: native es-PE
- reviewed
- 250+Languages and dialectsIncluding low-resource languages where good training data is scarce
- ZeroCrowdsourcingA private, managed workforce. Every annotator is background-checked.
- ISO 17100Vetted linguistsNative-language annotators qualified to the international standard
- ExpertRLHFLegal, medical, and technical specialists rank and score model output
Linguistic ground truth
Machines label words. People label meaning.
Automated pre-labeling is fast, and it is confidently wrong about sarcasm, idioms, and context. Every one of those errors becomes noise your model learns from. Our linguists label what the speaker meant.
“Great, another delay. Just what I needed.”
sentiment: positive · 0.91sentiment: negative · sarcasm · intent: complaint“No es moco de pavo.” Spanish idiom
literal: “it’s not turkey snot” · topic: foodmeaning: “it’s no small thing” · emphasisWhat we label
Three modalities. One standard of truth.
Text, images, and speech, labeled by people who speak the language and know the domain.
01 · Natural language processing
NLP and LLM training data
- Named entity recognitionEntities, relationships, and sentiment tagged across 250+ languages.
- RLHFExpert ranking and scoring that aligns model output with human intent.
- Intent and sentimentSarcasm, slang, and regional idioms, labeled by native speakers.
02 · Computer vision and OCR
Images, video, and scans
- OCR verificationText in images and scanned documents, corrected and labeled.
- Image and video taggingFrame-by-frame annotation for object detection and behavior analysis.
03 · Audio and speech
Speech recognition and TTS
- Multilingual transcriptionVerbatim and clean transcripts for training speech recognition (ASR).
- Phonetic labelingAccents, dialects, and prosody for natural-sounding text-to-speech (TTS).
Vertical-specific RLHF
Be the reviewer. Which answer ships?
Fine-tuning for law, medicine, or engineering takes reviewers who know the field. Our legal, medical, and technical experts rank model output, score it against a rubric, and explain why, so your model learns the logic and the terminology of your industry.
Prompt · legalCan my landlord keep my security deposit for normal wear and tear?
Choose the response you would rank higher.
You agree with our legal reviewer.Our legal reviewer ranked B higher.
- AccuracyA 2 · B 5
- Safety and hedgingA 1 · B 5
- ClarityA 4 · B 4
Reviewer note: A is confident and wrong. Normal wear and tear is generally not deductible, and A gives no jurisdiction caveat.
Secure, governed pipeline
Your data is your edge. We treat it that way.
No open crowd, no anonymous workers. A private, managed workforce works inside secure, audited infrastructure, and personal information is removed before anyone labels a thing.
- Private, managed workforce
- Background-checked annotators
- PII redaction first
- Diverse sourcing to reduce bias
Scope and guidelines
We agree the label schema, edge cases, and quality targets with your team before a single item is labeled.
PII redaction
Personal information is redacted or anonymized as the first step, before annotation begins.
Expert annotation
Native-language linguists and subject-matter experts from our private, managed workforce do the labeling.
Quality review
Labels are reviewed against your guidelines, and disagreements are resolved before delivery.
Secure delivery
Datasets are returned through secure, audited infrastructure, in the structure your pipeline expects.
250+
Languages, including the hard ones
Low-resource languages and rare dialects are where models fail and good data is hardest to find. That is where native annotators matter most.
- Spanish
- Quechua
- Arabic
- Tigrinya
- Mandarin
- Hmong
- Hindi
- Pashto
- Japanese
- Dari
- Portuguese
- Haitian Creole
- French
- Somali
- German
- Yoruba
- Korean
- Khmer
- Vietnamese
- Uyghur
- Russian
- Wolof
- Tagalog
- Kurmanji
Low-resource language
Scope a dataset
Better models start with better data.
Tell us the modality, the languages, the volume, and what the model needs to learn. We propose a label schema, a quality plan, and a timeline, and we can start with a pilot batch.
Working with personal data? Say so up front. Redaction and anonymization are planned as the first step.
Data requestExample details
- Dataset
- Customer support transcripts
- Volume
- 40,000 utterances, 12 languages
Related services
More language services for AI teams
Data, transcripts, and translation from one secure partner.
FAQ
AI data labeling questions, answered
Don’t see your question? A language specialist can answer it in one call.
Talk to a specialist
Get a straight answer
Tell us what you need translated or interpreted, and we’ll scope it with you.
Meet one-on-one with a language services expert, at a time that suits you.
Do you use crowdsourcing for data labeling?
No. Unlike open crowd platforms, we use a private, managed workforce. Every annotator is background-checked and works inside our secure, audited infrastructure, which protects both data integrity and confidentiality.
How do you handle personally identifiable information (PII)?
PII redaction and anonymization are the first step of the labeling process, before any annotator works on the data. We design the handling with you to support your obligations under privacy laws such as GDPR, HIPAA, and CCPA.
Can you help fine-tune an LLM for a specific industry?
Yes. We specialize in vertical-specific RLHF. Legal, medical, and technical experts rank, score, and correct model output so the model learns the logic and terminology of your industry.
What languages do you support for NLP training data?
More than 250 languages, including low-resource languages and rare dialects where high-quality training data is traditionally hard to find.
What is RLHF?
Reinforcement learning from human feedback (RLHF) trains a model using human judgments of its output. Reviewers compare or score responses against a rubric, and those preferences teach the model which answers are accurate, safe, and helpful.
What is named entity recognition (NER) annotation?
NER annotation marks the people, organizations, places, dates, products, and other entities in text, along with the relationships and sentiment around them. Native-language annotators get names, honorifics, and local formats right, which automated tools often miss.
Why use human annotators instead of automated labeling?
Automated labeling is fast but misreads sarcasm, idioms, slang, and context, and every error becomes noise the model learns from. Human ground truth from native speakers captures intent and cultural subtext, which improves accuracy and reduces bias.
How do you reduce bias in training data?
We source annotators and data across regions, dialects, and demographics, so the dataset represents the people your model will serve, and we flag skewed or unrepresentative samples during review.
Can you label images, video, and scanned documents?
Yes. We verify and correct OCR output in images and scanned documents, and tag images and video frame by frame for object detection and behavior analysis.
Do you provide speech data for ASR and TTS?
Yes. We produce verbatim and clean multilingual transcription to train automatic speech recognition, and phonetic labeling of accents, dialects, and prosody for natural-sounding text-to-speech.
Tell us what’s at stake. We’ll show you how we’d handle it.
Send your files or describe your event. A language specialist replies with a fixed quote and a delivery date, with no obligation.
Prefer to call? 720-325-0459