Meta Description: Compare the top NLP and text annotation companies in 2027 for NER, sentiment, intent, document labeling, search relevance and multilingual text data work.
Text is still the raw material behind most production AI. Chat assistants need intent labels, contract and claims systems need entities and relations pulled out of long documents, search teams need human relevance judgments to tune ranking, and every language model needs evaluation by people who can read the output in context. Most of that work is too large, too specialized or too multilingual to run fully in-house, so NLP and document AI teams lean on managed annotation providers.
This list focuses on providers with specific, published text and NLP offerings: named entity recognition (NER), sentiment and intent labeling, text classification, relation extraction, document and OCR labeling, search relevance rating and multilingual text. Several of them also run LLM evaluation programs, and we note that where it sits alongside the core text work. If your main need is preference data and fine-tuning for frontier models, our list of RLHF and LLM training data companies is the better starting point.
Every company below was checked against its own website and, where relevant, public announcements as of October 2026. We looked at the text tasks each one names, the languages it covers, its security certifications and any ownership changes that affect buyers. For a broader view of the market across images, video and audio, see our main guide to the top data annotation and labeling companies.
NLP And Text Annotation Companies At A Glance
Company
Headquarters
Best For
Core Services
Innodata
Ridgefield Park, New Jersey, USA
Entity, relation and event annotation on complex documents
NER, relation and co-reference annotation, document classification, Document Intelligence platform
iMerit
San Jose, California, USA
Platform plus managed workforce for text and PDF labeling
Sentiment, intent, NER and entity linking, document analysis, Ango Hub
Appen
Sydney, Australia and Kirkland, Washington, USA
Large multilingual text and relevance programs
Intent, NER and relation extraction, sentiment and toxicity, relevance rating
TELUS Digital
Vancouver, Canada
Search and ads evaluation at very high volume
Classification, NER, keyphrase extraction, QA, summarization, search and ads ratings
Shaip
New York, USA (part of Ubiquity)
Domain NER with knowledge base linking and form labeling
NER with UMLS and ICD-10 linking, relation triplets, coreference, OCR post-editing
Relevance testing and text classification from a language services firm
NER, sentiment, intent, toxicity, search, product and ad relevance
Label Your Data
Wilmington, Delaware, USA and Nicosia, Cyprus
Smaller teams that want published rates and a free pilot
Text classification, NER, sentiment, text comparison, OCR
Best NLP And Text Annotation Companies In 2027
1. Innodata — Separate Workbenches For Entity, Relationship And Multi-Label Annotation
Innodata was founded in 1988 in New York City and trades on Nasdaq under the ticker INOD. It is now based in Ridgefield Park, New Jersey, and describes a pool of more than 11,500 in-house subject matter experts working from 20+ delivery locations in 120+ languages and dialects. Its long history in content and publishing operations shows in how it approaches text: the company has spent decades structuring legal, scientific and financial documents, and much of its NLP work grows out of that.
The text annotation offering is unusually granular. Innodata lists dedicated Entity Annotation, Relationship Annotation and Multi-Label Annotation workbenches, and covers NER, sentiment analysis, intent identification, content classification, document and record classification, event annotation, co-reference annotation and content detection for toxicity or hate speech. Its case studies include annotating millions of pages of scientific data for drug discovery and thousands of articles for financial risk work, plus a social media project that tracked search query relevance metrics.
For document AI teams, Innodata also offers a Document Intelligence platform with pre-trained models for extracting data from complex documents, which can sit alongside managed labeling. On the generative side it runs model safety, evaluation and red teaming, AI benchmarks and agentic AI evaluation. Innodata is a good fit for teams whose text is long, technical and full of relationships that a simple tagging tool would miss, such as knowledge graph building, regulatory content or research literature.
2. iMerit — 20 Million Text Data Points Annotated At 97% Accuracy
iMerit was founded in 2012 by Radha Basu, who previously spent 20 years at HP, and is headquartered in San Jose, California, with offices in New Orleans, Kolkata and Bengaluru. The company describes a pool of more than 10,000 active resources across 60+ countries and lists SOC 2, ISO 27001, GDPR, HIPAA and TISAX compliance. Its text annotation page states that iMerit has annotated 20 million text data points at 97% accuracy.
The named text services cover sentiment analysis for product reviews, financial news and social media, intent analysis for chatbots and digital assistants, NER with entity classification and linking, part-of-speech tagging and document analysis for intelligent document processing. iMerit also mentions search relevance improvement among its NLP applications, and it publishes case studies on linguistic and copy-editing corrections and on structuring taxation data for extraction. Work typically runs through a two-step production and QA workflow.
What separates iMerit from pure service shops is Ango Hub, its own annotation and model evaluation platform, which handles text, PDF and audio alongside image, video, LiDAR and DICOM. Teams can use the platform with iMerit’s managed workforce, which helps when text and document projects share a pipeline with other data types. iMerit also runs iMerit Scholars, a network of 25,000+ domain experts, and a Deep Reasoning Lab for chain-of-thought and RLHF work, so it can extend from classic NLP labeling into LLM evaluation when needed.
3. Appen — Search Relevance Rating Since 2003 Across 235+ Languages
Appen was founded in Sydney in 1996 and is listed on the Australian Securities Exchange (ASX: APX), with its US headquarters in Kirkland, Washington. It says it has more than 1 million vetted contributors in 170+ countries, supports 235+ languages and 500+ locales, and holds SOC 2 and ISO 27001 certification. Its own timeline traces text work back to early NLP systems in 1996 and human evaluation for web search ranking models in 2003, which makes it one of the longest-running relevance rating providers in the market.
Appen lists five core text and NLP annotation types: intent classification, named entity and relation extraction, sentiment and toxicity labeling, relevance rating, and instruction-response pair construction for LLM training. Inter-annotator agreement thresholds are set per project during scoping rather than quoted as a single figure, which is a sensible approach for tasks such as sentiment where acceptable agreement varies by label set. The company also built curated text datasets for language models from 2020 onward and multilingual annotation across 100+ language pairs as early as 2006.
Search, ads and e-commerce teams will find Appen’s crowd scale useful for relevance programs that need many raters per locale. On the model side, Appen offers hallucination benchmarking, bias detection, calibrated LLM-as-a-judge deployment and custom benchmarking. Appen is best suited to large, ongoing programs where language coverage and rater volume matter more than deep single-domain expertise.
4. TELUS Digital — More Than 2 Billion Labels Delivered Annually
TELUS Digital, formerly TELUS International, is headquartered in Vancouver and was founded in 2005. Its AI data business was built largely through acquisition: TELUS International agreed to buy Lionbridge AI for US$935 million in 2020 and added Bengaluru-based annotation company Playment in 2021. The company was renamed TELUS Digital in 2024, and on October 31, 2025, TELUS Corporation completed its purchase of the shares it did not already own, making TELUS Digital a wholly owned subsidiary.
TELUS Digital says it delivers more than 2 billion labels a year through a global AI community of 1M+ people, with annotation in 500+ languages and dialects. Its text annotation work covers classification, named entity recognition, keyphrase extraction, intent classification, question answering, sentiment analysis and text summarization. It also draws on 20+ expert domains, including STEM, law, medicine and finance, for tasks that need specialist readers.
The clearest differentiator for search teams is a dedicated Data for Search & Ads practice covering ads ratings and AI search evaluations, backed by regional and local expertise. That combination of rater volume and locale coverage suits search engines, marketplaces and ad platforms that need continuous human judgments in many markets. TELUS Digital was named a Leader in Everest Group’s Data Annotation and Labeling PEAK Matrix Assessment, and it also offers red teaming and safety evaluations for generative AI.
5. Shaip — NER With Linking To Wikidata, UMLS And ICD-10
Shaip has been operational since 2019 and grew out of a project with a Fortune 10 company. In February 2026 New York-based Ubiquity Global Services announced an agreement to acquire Shaip, which now operates as a specialized AI data platform within Ubiquity. Shaip lists 150+ Shaip team members, 10,000+ in-house experts, 12 delivery centers on four continents and coverage of 150+ languages and dialects, and it states GDPR, HIPAA, SOC 2 Type II, ISO 9001 and ISO 27001 for the Shaip Data Platform.
Shaip’s text annotation menu is one of the most detailed on this list. It covers single-label, multi-label and hierarchical text classification, NER with entity linking to knowledge bases such as Wikidata, UMLS and ICD-10, sentiment and emotion labeling, intent annotation, relation annotation through subject-action-object triplet extraction, and linguistic annotation including part-of-speech tagging, morphological tagging and dependency parsing. It also handles coreference resolution across sentences and full documents.
For document AI, Shaip offers document annotation with OCR post-editing, applying field-level labels to scanned PDFs, invoices and forms. Its annotator pool spans Indo-European, Sino-Tibetan, Afroasiatic and Austronesian language families, including low-resource Indic and African languages. The knowledge base linking makes Shaip a strong option for clinical NLP, and teams in that space should also review our list of medical data annotation companies. Shaip says labeled data can typically be delivered in 7 to 14 days.
6. Cogito Tech — Six Compliance Badges Including ISO 27001, SOC 2 And HIPAA
Cogito Tech is based in Levittown, New York, and cites 11 years of experience in AI training data. Its NLP team works in 35+ languages, and the company lists GDPR, ISO 9001, ISO 27001, SOC 2, CCPA and HIPAA compliance, which matters for teams handling customer messages, health records or financial text. It also promotes DataSum, its own certification program for data quality and sourcing.
The text and NLP service list is broad. Cogito Tech names sentiment analysis, named entity recognition, part-of-speech tagging, text classification, relation extraction, intent classification, feature classification, chatbot training and machine translation, and its FAQ adds language modeling, summarization, information extraction and text generation support. It frames this work around six industries: healthcare, marketing, government, finance, e-commerce and education.
On the document side, Cogito Tech offers OCR transcription and intelligent document processing, which suits teams digitizing forms, receipts or archives before training extraction models. It also provides RLHF, red teaming, fine-tuning data and prompt and response generation for generative AI projects. Cogito Tech is a practical choice for mid-sized NLP programs that want one vendor to cover classic text labeling, document transcription and some LLM data work under a single compliance framework.
7. LXT — 1,000+ Language Locales Backed By The clickworker Crowd
LXT is headquartered in Toronto and was founded in 2010. In December 2024 it announced an agreement to acquire Germany’s clickworker, a crowdsourcing platform with more than 6 million freelancers, and the combined business now cites 10M+ contributors, 250K+ domain experts and a network across 150+ countries. LXT covers more than 1,000 language locales and operates five ISO 27001 certified secure facilities.
Its text annotation list reads like a linguist’s checklist: named entity tagging, sentiment annotation, intent annotation and intent classification, toxic language identification, keyword annotation, grammatical markup, dialog analysis, domain annotation, linguistic annotation and pronunciation dictionary creation. It also offers text summarization, caption creation and validation, content evaluation and localization. Search relevance is listed as a data evaluation service.
LXT’s main advantage is language breadth. Teams building NLP models for markets that larger providers treat as long tail, or projects that need pronunciation lexicons and grammatical markup alongside entity labels, will find more native coverage here than at most competitors. The clickworker crowd adds volume for simpler judgment tasks, while LXT’s secure facilities handle sensitive text that cannot leave a controlled environment.
TrainAI is the AI data services unit of RWS, a UK-headquartered language, content and intellectual property services company listed on the London Stock Exchange’s AIM market. RWS launched TrainAI in February 2023, and the unit now cites more than 100,000 active, vetted AI data specialists working in 400+ language variants across 175+ countries. That language services background shapes how it approaches text: many of its annotators are linguists first.
On the NLP side, TrainAI lists intent classification, sentiment analysis, entity recognition for names, dates and locations, and utterance and dialog data for chatbots and virtual assistants. Its search evaluation service is described in more detail than most: evaluators assess the accuracy and relevance of results for specific queries, flag spam and low-quality or misleading content, and evaluate search features such as autocomplete, query suggestions, related searches and knowledge panels.
TrainAI also provides generative AI services including prompt engineering, response rating, fact verification, red teaming and locale evaluation. It is a good match for search and assistant teams that need relevance judgments in many locales from raters who understand local language and culture, and for organizations already using RWS for translation that want to keep multilingual data work with one supplier.
9. Lionbridge — Search, Product And Ad Relevance Testing Through Aurora AI Studio
Lionbridge is a language services company based in Waltham, Massachusetts, with more than 25 years in the business and solution centers in 24 countries. It sold its original AI division, Lionbridge AI, to TELUS International in 2020, and has since rebuilt an AI training practice around its Aurora AI Studio platform and a community of more than half a million testers, reviewers and linguists. Buyers comparing vendors should note that Lionbridge’s current AI training work is separate from the business TELUS now owns.
Lionbridge’s text and LLM services include named entity recognition, sentiment analysis, intent classification, toxicity classification, content classification and response evaluation and scoring. Under output validation it lists search, product and ad relevance, plus fact and relevance testing of model output, hallucination detection, preference ranking and safety review. Its broader AI training work also covers data curation, taxonomy development and cultural adaptation.
The multilingual depth is the main draw. Lionbridge case studies cite work in 60+ underrepresented languages and 50+ ultra-rare locales, and one project sourced and trained 4,000 native speakers. That makes Lionbridge a sensible option for e-commerce and search teams that need product and query relevance judged by native speakers in many markets, and for teams that want taxonomy design and labeling handled by the same group.
10. Label Your Data — Published NLP Rates From $0.02 Per Entity
Label Your Data has offices in Wilmington, Delaware, and Nicosia, Cyprus, and describes a team of about 1,000 people across 22 countries serving 200+ customers. It holds ISO 27001 certification and runs GDPR compliant workflows. On review platforms it shows a 5.0 rating on Clutch from 27 reviews and 4.9 on G2 from 21 reviews, as listed on its site.
Its text annotation services cover text classification for spam detection, news topics and intent detection, named entity recognition for names, locations, brand mentions and medical or legal document processing, sentiment analysis for social media and customer feedback, and text comparison for plagiarism, duplicate content and legal document comparison. It also offers OCR as a separate service and works in 55+ languages.
Label Your Data is one of the few providers in this space that publishes rates. Its pricing page says NLP work starts from $0.02 per entity and that time-billed projects typically run at $6 per annotator hour, with no setup fees and a free pilot on your own data before you commit. Estimates can be built on entities, characters or annotation hours. That transparency makes it a practical fit for startups and smaller NLP teams that need predictable budgets. For context on how labeling fits into total project spend, see our guide to AI development cost.
How To Choose An NLP And Text Annotation Company
Does The Vendor Name The Exact Tasks You Need?
“Text annotation” covers everything from simple topic tags to dependency parsing and coreference chains. Ask for the specific tasks in writing and check that the vendor has done them before: nested or overlapping entities, entity linking to a knowledge base, relation triplets, multi-label versus hierarchical classification, or field-level labeling on scanned forms. Providers that list these separately, as Innodata, Shaip and Cogito Tech do, usually have tooling and guidelines already built for them, which shortens setup.
How Will Quality Be Measured On Subjective Labels?
Sentiment, intent and relevance are judgment calls, so a single accuracy figure says little. Ask how the vendor measures inter-annotator agreement, how many raters see each item, how disagreements are adjudicated and how guideline changes are rolled out mid-project. A short paid or free pilot on your own data, scored against a gold set you control, is the most reliable way to compare providers before signing a volume contract.
Are The Annotators Native Speakers With The Right Domain Knowledge?
Multilingual NLP fails quietly when labels come from second-language speakers or general crowds working on specialist text. Check how many native annotators the vendor actually has in your priority locales, not just the total language count on its homepage. For legal, clinical or financial text, ask whether annotators have domain training and how that is verified. Language-heavy providers such as LXT, RWS TrainAI and Lionbridge tend to be strongest on locale depth.
Can The Vendor Handle Documents, Not Just Sentences?
Document AI projects need annotators who can work with page layout, tables, scanned images and long context. Ask whether the vendor can label directly on PDFs, correct OCR output, mark key-value pairs and handle multi-page relationships. If you are also choosing a labeling tool, our list of AI data annotation platforms covers software options that pair with managed services.
What Security And Data Handling Controls Apply To Your Text?
Customer emails, support transcripts, contracts and health records often contain personal data. Confirm which certifications cover the specific facilities and workflows used for your project, whether work can be done in secure centers rather than by remote crowds, how data is deleted after delivery and whether the vendor’s ownership has changed recently. Acquisitions such as Ubiquity’s agreement to buy Shaip and LXT’s purchase of clickworker can affect contracts, subprocessors and data residency.
Conclusion
The strongest NLP and text annotation providers in 2027 are the ones that name their tasks precisely and can show how they handle the hard parts: overlapping entities, relations across long documents, subjective sentiment and relevance labels, and languages beyond the usual top ten. Innodata, Shaip and Cogito Tech stand out for task depth on complex documents, Appen, TELUS Digital, RWS TrainAI and Lionbridge for search relevance and multilingual rater programs, iMerit for combining a platform with managed teams, LXT for long-tail language coverage and Label Your Data for transparent pricing.
Start with a clear list of tasks and label definitions, run a pilot with two or three providers on your own data, and compare agreement scores and turnaround rather than headline accuracy claims. For a wider view of providers across every data type, return to our guide to the top data annotation and labeling companies.
