Text Data Collection Services for AI Training
We source, curate, and deliver high-quality text datasets that form the foundation of language models, NLP systems, and document AI. From large-scale web corpora and domain-specific document collections to human-written prompt-response pairs and multilingual content — our text data collection service is built for teams that refuse to compromise on data quality.
- 25+ crowdsourcing platforms
- 30+ industries
Our Expertise
Industry use cases
- LLM pre-training
- language model fine-tuning
- general-purpose NLP benchmarking
Industry use cases
- domain-adapted LLMs
- contract analysis AI
- clinical NLP
- financial document understanding
Industry use cases
- instruction-tuned LLMs
- RLHF pipelines
- chatbot fine-tuning
- evaluation benchmarks
Industry use cases
- conversational AI
- dialogue state tracking
- chatbot training
- customer support automation
Industry use cases
- multilingual NLP models
- cross-lingual transfer learning
- machine translation training
- global product localization
Industry use cases
- data augmentation for low-resource tasks
- adversarial robustness testing
- synthetic preference data generation
Industry use cases
- named entity recognition, intent detection
- text classification
- information extraction
- semantic parsing
Text Data Collection Methods
Text Data Collection Platforms and Tools
Crawling & extraction
Scrapy, Playwright, Apache Nutch, custom domain-focused crawl infrastructure
Quality filtering
fastText (language ID), KenLM (perplexity scoring), custom deduplication (MinHash, exact-match), PII detection pipelines
Annotation platforms
Label Studio, Prodigy, Doccano, Scale AI; proprietary RLHF preference annotation interface
Crowdsourcing
Appen, proprietary contributor platform with task routing and quality gating
Storage & delivery
Plain text, JSONL, Parquet, HuggingFace Datasets format, or any custom schema; UTF-8 with full Unicode support across all scripts
Project Steps
01
Discovery & requirements scoping
- We define domain coverage, language targets, content type, annotation schema, volume, and quality thresholds. Licensing, consent, and data provenance requirements are documented and agreed upfront.
02
Data strategy & source mapping
- We identify the best mix of crawled, authored, licensed, and synthetic text to meet your coverage requirements, and design a sampling plan that ensures topical, stylistic, and linguistic diversity.
03
Pilot collection & annotation review
- A sample batch is collected, processed, and annotated. Quality metrics — annotation accuracy, text quality scores, label distribution balance — are reviewed with your team before scaling.
04
Full-scale collection & annotation
- Production pipelines run across all source channels. Annotation campaigns run in parallel, with daily monitoring of output volume, label accuracy, and inter-annotator agreement scores.
05
Quality assurance
- Automated quality filters (deduplication, perplexity, PII scan, language verification) run alongside human expert review. Edge cases and low-confidence labels are escalated for adjudication.
06
Delivery & ongoing support
- Datasets are delivered in your preferred format with full metadata — source, language, domain, annotation labels, and provenance records. We support iterative dataset extension, targeted gap-filling batches, and long-term content partnership agreements.
Frequently Asked Questions
From which data sources can you collect information?
Depending on the task, we draw on consented participants, crowdsourcing platforms, in-house data specialists, client-provided text files and documents, web scraping of legally permissible public sources, and licensed or off-the-shelf datasets.
How do you manage consent, privacy, and data usage rights?
Before launch, we define the lawful basis or source permission, notices and consent where required, permitted uses, retention period, and transfer conditions for text containing personal or identifiable information. Personal data is minimized and can be pseudonymized or anonymized where appropriate, with exact controls depending on the data category and jurisdictions involved.
How do you ensure data security?
Security controls are defined for each project based on the text data type and client requirements. Depending on scope, they may include NDAs, role-based access, secure data transfer and storage, data minimization, and agreed retention or deletion rules, with our collection processes structured to support privacy compliance across the relevant jurisdictions and project scope.
Can you annotate the text you collect?
Yes. Collection, validation, and text annotation can be delivered as one workflow, so the same technical specification and metadata scheme carry through to labeling. Our annotators support entity recognition, entity tagging, intent classification, text classification, sentiment analysis, and text summarization, applying labeling services suited to each annotation task.
Can text be collected in multiple languages?
Yes. We support multilingual text collection across multiple languages, subject to source availability and local legal constraints. The specification can define language, dialect, domain, and reviewer qualifications, with native-speaking linguists involved for tasks like linguistic analysis and translation-adjacent text mining.
How do you ensure data quality during collection?
Quality is controlled at three stages: before launch, during collection, and before delivery. We validate the specification and pilot, monitor sourcing and labeling during the collection process, and run automated checks alongside human review against agreed thresholds for text classification and character recognition accuracy.
Ready to get started?
Tell us what you need — we’ll reply within 24h with a free estimate
- Andrew
- Head of Client Success
— I'll guide you through every step, from your first
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