AI Text Data Collection

The language data that makes NLP & LLMs understand humans

End-to-end text data collection and annotation — from domain-specific corpus sourcing to RLHF preference labeling and fine-grained NER — across 40+ languages, built for NLP, conversational AI, and large language model training.

customer_support_corpus_v3.jsonl

Sarah Chen PERSON from Meridian Health ORG contacted support regarding a billing issue with her account in Austin, Texas LOC. She described the experience as completely unacceptable NEG and requested an immediate resolution.

After reviewing her case, the Billing Department ORG confirmed the error and issued a full refund within 24 hours. Ms. Chen PERSON replied that she was very satisfied POS with the outcome and would continue using the service.

Trusted by AI teams worldwide

50M+

Tokens annotated

98.5%

Average QA accuracy

40+

Image Categories

2K+

Domain expert annotators

48h

Pilot batch turnaround

Use cases

Text datasets for every NLP application

From training foundational language models to fine-tuning domain-specific classifiers — Synnth builds the labeled text data your model actually needs.

LLM Fine-Tuning & Instruction Tuning

Carefully crafted instruction–response pairs, multi-turn dialogue datasets, and domain-adapted conversation data for fine-tuning foundation models on enterprise tasks.

Instruction pairs, Multi-turn, Domain adaptation, RLHF-ready

RLHF & Human Preference Data

Response ranking and preference labeling by trained human annotators — rating outputs on helpfulness, accuracy, safety, and coherence to align LLMs with human values.

Preference ranking, Pairwise comparison, Safety labels, Helpfulness scoring

Named Entity Recognition (NER)

High-precision entity span annotation across custom taxonomies — from standard PERSON/ORG/LOC to domain-specific entities in legal, medical, and financial text.

Custom taxonomies, Nested entities, Relation extraction, CoNLL format

Conversational AI & Chatbot Training

Intent classification, slot filling, dialogue act annotation, and multi-turn conversation datasets for building customer service bots, virtual assistants, and task-oriented dialogue systems.

Intent labels, Slot tagging, Dialogue acts, Multi-turn

Sentiment & Emotion Analysis

Fine-grained sentiment labeling at document, sentence, and aspect levels — plus emotion classification across discrete categories for social media, review, and CX analysis models.

Aspect-based, Fine-grained, Emotion taxonomy, Multi-label

Machine Translation & Multilingual NLP

Translation quality evaluation, post-editing, and parallel corpus creation across 40+ language pairs — by native-speaker translators, not MT systems with human rubber-stamps.

MTPE, Parallel corpora, QE annotation, 40+ languages

RLHF & LLM alignment

Human preference data that actually aligns your model

Reinforcement Learning from Human Feedback is only as good as the humans doing the feedback. Synnth recruits and trains annotators specifically for RLHF — calibrated on your model’s output domain, not general-purpose crowd workers.

Domain-calibrated annotators — trained on your model's outputs, your rubric, and your quality bar. Not a fresh crowd for every batch.
Multi-dimensional scoring — helpfulness, accuracy, safety, fluency, coherence — rated independently so you can weight each dimension in your reward model.
High inter-annotator agreement — IAA targets above 0.80 Cohen's kappa with disagreement adjudication workflows for borderline cases.
Red-teaming & adversarial prompts — we also source adversarial prompt datasets for safety testing, jailbreak research, and refusal training.

HUMAN PREFERENCE ANNOTATION — EXAMPLE TASK

PROMPT

Explain the difference between supervised learning and reinforcement learning in simple terms, as if to a non-technical manager.

RESPONSE A Preferred ✓

Supervised learning is like training with an answer key — you show the model many examples with correct answers. Reinforcement learning is like training a dog: you reward good behavior and ignore bad...

RESPONSE B

Supervised learning utilizes labeled datasets to train models via gradient descent on a loss function, while reinforcement learning employs a Markov decision process with reward signals...

Helpfulness: 5/5 Accuracy: 4/5 Fluency: 5/5 Safety: pass

What we collect & annotate

The complete text data pipeline, end-to-end

From corpus sourcing and data generation to fine-grained annotation and
QA delivery — Synnth manages every stage of the text data lifecycle.

Text collection

Domain corpus sourcing - web, academic, legal, medical, financial, and proprietary document sourcing.
Human-written text generation - native speakers producing prompts, responses, and seed documents to spec.
Instruction–response pair creation - expert writers crafting instruction tuning datasets for LLM fine-tuning.
Conversational data collection - multi-turn dialogues for chatbot and assistant training.
Adversarial & edge-case text - red-teaming prompts, jailbreak attempts, and safety boundary cases.
Multilingual parallel corpora - source + target translation pairs created by native-speaker translators.
Social media & UGC text - licensed social text covering informal registers, slang, and code-switching.

Annotation & labeling

Named entity recognition - standard and custom entity taxonomies, nested spans.
Sentiment & emotion tagging - document, sentence, and aspect-level; discrete and continuous.
Intent & slot annotation - dialogue-act labeling for task-oriented NLP systems.
Relation extraction - binary and n-ary relations between entity pairs.
RLHF preference ranking - multi-dimensional response comparison for reward model training.
Coreference resolution - mention detection and chain linking within and across sentences.
Translation quality evaluation - MQM error annotation and fluency/adequacy scoring.

How it works

From brief to production-ready text dataset

A transparent four-stage pipeline with quality gates at every step — designed
for NLP teams who need reliable, repeatable delivery.

Define scope

Share your NLP task, domain, label taxonomy, language targets, and inter-annotator agreement requirements. We co-design annotation guidelines and calibration tests with your team.

Source & generate

We source existing corpus material or commission human-written text matched to your domain, register, and language requirements — with provenance and consent documentation.

Annotate & QA

Expert, domain-matched annotators label your text. IAA is measured on calibration samples, disagreements are adjudicated, and senior reviewers sign off before every delivery.

Deliver & iterate

Receive clean datasets in JSON, JSONL, CSV, CoNLL, or your custom schema — alongside a full QA report showing IAA scores, rejection rates, and annotator calibration stats.

Why Synnth

Built for teams who can't afford noisy labels

Six reasons ML teams choose Synnth over generic labeling platforms and
crowdsourcing pipelines for text annotation.

Domain-expert annotators

Legal text annotated by legal professionals. Medical records by clinicians. Financial documents by finance specialists. Domain expertise isn’t optional — it’s the difference between useful labels and noise.

200+ specialists

IAA-driven quality

Inter-annotator agreement is measured on every project, not just spot-checked. We target IAA above 0.80 (Cohen’s kappa) on standard tasks, with adjudication workflows for borderline cases.

0.85 avg. IAA

Native-speaker annotators

For multilingual NLP projects, every language is annotated by native speakers — not translators or speakers annotating in a second language. Dialect knowledge and register sensitivity built in.

40+ languages

Custom ontology design

We don’t hand you a generic label set. We co-design entity taxonomies, sentiment scales, and dialogue act schemas with your ML team — then build annotator training around your exact edge cases.

Enterprise-grade security

All text data encrypted at rest and in transit. GDPR compliant, HIPAA-ready for healthcare NLP. NDAs on every engagement. Your proprietary documents and model outputs stay in controlled environments.

Fast pilot SLAs

Validate annotation quality before committing to full production volume. Pilot batches of up to 5,000 documents in 48–72 hours, with full QA reports and the same annotators who will run production.

48h pilot

Language coverage

40+ languages, with native-speaker annotators for each

Multilingual NLP requires native fluency — not machine translation with
human sign-off. Every language we support is annotated by people
for whom it’s a first language.

English (US/UK/AU/IN) Hindi Mandarin Chinese Spanish (LA/ES) Arabic (MSA + dialects) French German Portuguese (BR/PT) Japanese Korean Bengali Urdu Telugu Tamil Marathi Gujarati Punjabi Kannada Malayalam Italian Dutch Polish Turkish Russian Swedish Norwegian Danish Finnish Vietnamese Thai Indonesian Malay Tagalog Swahili Hausa Hebrew Persian (Farsi) Amharic Yoruba + custom on request

Output formats

Delivered in the format your pipeline already expects

No conversion needed. Datasets arrive ready to plug into your training infrastructure.

JSON JSONL CSV CoNLL-2003 BRAT Standoff Label Studio JSON Prodigy JSONL Doccano JSON Hugging Face Datasets Parquet XML TSV SQuAD JSON Custom schema

FAQ

Common questions about AI text data collection

Everything you need to know before starting a text annotation project with Synnth.

💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.

What is AI text data collection?

AI text data collection is the process of sourcing, generating, and curating written text specifically to train NLP and LLM models. This includes building labeled datasets for tasks like text classification, named entity recognition, sentiment analysis, question answering, instruction tuning, and RLHF preference alignment. The quality and diversity of labeled text data is the primary determinant of NLP model performance.

What is RLHF data collection and how does Synnth approach it?

RLHF (Reinforcement Learning from Human Feedback) data collection involves trained human annotators comparing pairs of model outputs and indicating which is better — on dimensions like helpfulness, accuracy, safety, and fluency. This preference data trains a reward model used to fine-tune the LLM. Synnth recruits and calibrates RLHF annotators on your specific model’s output domain and quality rubric — not generic crowd workers — to ensure the preference signal is meaningful and consistent.

How does Synnth ensure annotation quality for subjective NLP tasks like sentiment?

For inherently subjective tasks, we invest heavily in annotator calibration: shared training, example anchors for edge cases, and calibration tests before annotators work on live data. We measure inter-annotator agreement (Cohen’s kappa or Fleiss’ kappa) on a statistically significant sample of every batch. Batches that fall below our IAA threshold are returned for re-annotation or adjudicated by senior reviewers. We share IAA scores and methodology in every delivery report.

Can Synnth collect domain-specific text data — legal, medical, financial?

Yes. Domain-specific text annotation is one of Synnth’s core strengths. Legal documents are annotated by legal professionals who understand contractual terminology and jurisdiction-specific concepts. Medical records are handled by annotators with clinical training. Financial text is annotated by finance specialists. We match domain expertise to annotation task — which is the primary difference between useful labels and label noise in specialized NLP.

What is the difference between instruction tuning data and RLHF data?

Instruction tuning data consists of (instruction, response) pairs used to supervised fine-tune a base LLM to follow instructions. RLHF data is used in a subsequent alignment phase — human annotators compare multiple model-generated responses and rank them, training a reward model that guides further fine-tuning via reinforcement learning. Both are valuable for different stages of LLM development, and Synnth can support both.

What languages does Synnth support for multilingual NLP datasets?

Synnth supports text annotation in 40+ languages, with native-speaker annotators for every language we offer. This includes all major world languages as well as regional languages such as Telugu, Tamil, Marathi, Swahili, Hausa, Yoruba, and Amharic. For low-resource languages not listed, contact us — we have an extended network for sourcing specialist annotators.

What output formats are text datasets delivered in?

We deliver in your preferred format: JSON, JSONL, CSV, CoNLL-2003, BRAT standoff, Label Studio JSON, Prodigy JSONL, Doccano JSON, Hugging Face Datasets format, Parquet, SQuAD JSON, and custom schemas. Format is agreed during the scoping phase with no additional cost for standard formats.

How is our proprietary text and model output data kept secure?

All text is transferred through encrypted channels (TLS 1.3) and stored with AES-256 encryption at rest. Annotation work is performed only within access-controlled, audited environments. We sign NDAs on every engagement and can operate under data processing agreements for regulated industries including healthcare, legal, and financial services. Your model outputs, proprietary documents, and annotation instructions are never used for any purpose beyond your project.

What is the minimum project size and turnaround time?

We accept pilot batches from 1,000 documents or annotation examples, typically delivered within 48–72 hours at full QA standards. Enterprise projects with ongoing delivery requirements are scoped with custom SLAs, dedicated project managers, and priority queue access to ensure consistent velocity.

Get started

Start your text data project today

Tell us your NLP task, domain, language targets, and annotation schema. Our team responds within one business day with a scoping plan and no-obligation quote.