Data Annotation Services · Text Annotation
Text Annotation Services for NLP
Named entities, relations, sentiment, intent, classification and document fields labelled by trained linguist annotators, including code-mixed and transliterated text.
- NER, sentiment & intent
- Many languages & scripts
- Pilot batch before scale
Text Annotation Services
Text Labels Built on Linguistic Judgement
Text looks easy to label until the edge cases arrive. Is "Apple" a company or a fruit in this review? Is a sarcastic five-star comment positive? Where does an address entity end when the pin code sits on the next line? Zilo AI's text annotation services put trained linguist annotators on these calls, with every label schema tested on a pilot and ambiguous cases sent for adjudication rather than guessed.
We label chats and support tickets, product reviews, search queries, social posts, contracts, invoices and forms, in English and in Indian and global languages, including code-mixed and transliterated text. Choose a managed project or dedicated annotators who work inside your tool, such as Label Studio, Prodigy, doccano or INCEpTION.
Entities and Relations
Named entity recognition, entity linking to your knowledge base, and the relations between entities.
Sentiment and Intent
Sentiment, emotion and intent at document, sentence or aspect level, to a label set you define.
Document Extraction
Fields, tables and clauses marked in contracts, invoices and forms for extraction models.
Multilingual Text
Native-speaker annotators for Indian and global languages, transliteration and code-mixed content.
What We Annotate
Text Annotation Tasks We Deliver
From span-level entities to whole-document classification and translation review, each task runs to your schema, with consensus and gold-standard checks before delivery.
NLP Data Labeler / Linguist Annotator
Applies linguistic judgment to text labeling: span-level entities, part-of-speech and syntax tags, text classification, relations and coreference, following detailed guidelines and flagging ambiguous cases for adjudication.
Multilingual Data Annotator
Labels and reviews text in multiple languages and scripts, handling transliteration, code-mixed content and locale-specific meaning, so classifiers, chatbots and LLMs perform consistently beyond English.
NER & Entity Linking
Identifies people, organizations, products, locations, dates and domain-specific entities, links them to knowledge-base IDs and annotates the relations between them for extraction, search and knowledge graph projects.
Sentiment & Intent Labeling
Classifies messages, reviews, tickets and queries by sentiment, emotion, intent, urgency and topic, including aspect-based and multi-label cases, to train support automation, analytics and conversational AI.
Translation & Localization QA
Reviews machine and human translations for accuracy, fluency, terminology and cultural fit, scores output against error typologies such as MQM, and post-edits content for training and evaluation datasets.
Document & Contract Annotation
Labels invoices, contracts, forms and reports for document AI, tagging key-value pairs, tables, clauses and document types so extraction models learn layouts, domain terms and the fields your workflows depend on.
For ML & Data Teams
How to Brief a Text Annotation Vendor
Text annotation quality is decided by how precisely the label schema is written. Two careful annotators will still disagree if the guideline does not say what to do with nested entities or mixed sentiment. Settle these points before you send text to any vendor, Zilo AI included.
Questions to ask any annotation vendor
- Who writes the edge-case rules, and how are disagreements between annotators resolved?
- Do you measure inter-annotator agreement, and will you share it per batch?
- Do your annotators read the language natively, including regional and code-mixed text?
- How do you handle text that fits no label, or more than one?
- Can you work inside our annotation tool and keep data in our environment?
- Which formats can you deliver, such as CoNLL, IOB, JSON or JSONL?
Define Every Label With Examples
Each entity type, intent or sentiment class needs a definition, two positive examples and one tricky negative one.
Decide on Nested and Overlapping Spans
Say whether "Bank of Baroda Mumbai branch" is one entity or two, and whether spans may overlap.
Handle Mixed and Neutral Cases
Write rules for mixed sentiment, sarcasm and text with no clear intent, or annotators will each invent their own.
Specify Languages and Scripts
List the languages, scripts and transliteration you expect, so the vendor staffs native readers for each.
Plan Adjudication
Decide who settles disagreements and how often guidelines are updated from those decisions.
Pilot and Measure Agreement
Label a pilot set with two annotators each and measure agreement. Low agreement points to a guideline gap, not a bad annotator.
Engagement Models
Flexible Ways to Work With Us
Outsource a complete annotation project or extend your in-house data team, whichever suits your pipeline.
Managed Projects
Share your data and guidelines, and we handle annotation, quality checks and delivery of AI-ready datasets on schedule.
Best forEnd-to-end outsourcing
Pilot Batch
Start with a small, calibrated sample to validate guidelines, accuracy and turnaround before committing to scale.
Best forNew projects & vendor evaluation
Dedicated Teams
Trained annotators work exclusively on your project, in your tools and workflows, with a single point of contact.
Best forOngoing, high-volume pipelines
Annotator Staffing
Hire trained annotators, raters and linguists on contract to extend your in-house data or AI team.
Best forScaling your own team
How It Works
From Raw Data to AI-Ready Data
A structured workflow built around your guidelines, with quality checks at every stage.
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1
Share
Send us sample data, your label taxonomy or guidelines, target volumes and preferred output format.
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2
Pilot
We annotate a calibration batch, align on edge cases and refine guidelines together with your team.
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3
Scale
Trained annotators work through your data at volume, with multi-level review and QA sampling.
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4
Deliver
Receive accurate, consistently labeled data in your format, and iterate as your models evolve.
Why Zilo AI
Why AI Teams Choose Zilo AI for Data Annotation
Good training data comes from a repeatable process, not a one-off delivery. We pair trained people with agreed guidelines and layered quality checks, and adapt to your tools, formats and engagement model instead of forcing ours.
- One partner for image, video, text, audio and LLM data
- Pilot batches and guideline calibration before scaling
- Multi-level review with gold-standard and consensus checks
- Delivery in COCO, YOLO, JSONL, SRT or your custom schema
- Secure, access-controlled workflows aligned to your data policies
- Managed projects or dedicated annotators on contract
Industries We Serve
- Autonomous Vehicles & Mobility
- AI Labs & LLM Developers
- E-commerce & Retail
- Healthcare & Life Sciences
- BFSI & Fintech
- Search & Discovery
- Manufacturing
- Media & Entertainment
- Agriculture
Multilingual Coverage
Our Global Language Expertise
Our trained annotators and language experts work across major global and regional languages, so your AI performs for a worldwide audience.
- German
- French
- Portuguese
- Spanish
- Italian
- Russian
- Polish
- Swedish
- Turkish
- Arabic
- Chinese
- Mandarin
- Malay
- Korean
- Dutch
- Greek
- Romanian
- Slovak
- Danish
- Croatian
- Czech
- Vietnamese
FAQs
Text Annotation Services: Common Questions
Have a question we have not answered? Talk to our team and we will get back to you quickly.
Talk to Our Team
- +91 77604 02792
- connect@ziloservices.com
- Mon to Fri: 8am to 10pm
- Hebbal Kempapura, Bangalore
What text annotation services does Zilo AI offer?
We provide named entity recognition and entity linking, relation and coreference annotation, sentiment and intent labelling, text classification, part-of-speech and syntax tags, document and contract annotation, and translation and localization QA.
Do you annotate text in Indian languages?
Yes. Our multilingual annotators work across Indian and global languages and scripts, including transliterated and code-mixed text such as Hinglish.
Which tools and formats do you use?
Our annotators work in Label Studio, Prodigy, doccano, INCEpTION or your own tool, and deliver in CoNLL, IOB, JSON or JSONL, or your custom schema.
How do you keep labels consistent across annotators?
Every schema is tested on a pilot first. During production we run consensus and gold-standard checks, and ambiguous cases go to adjudication rather than being guessed.
Can you label data for LLM training as well?
Yes. Alongside classic NLP labelling we provide RLHF, preference ranking, SFT demonstrations and AI response rating through our AI trainer and rater teams.
Can we start with a small batch?
Yes. Send a sample of your text and your label schema, and we will annotate a pilot batch for you to review before scaling.
Ready to Start Your Text Annotation Project?
Share a sample of your text and your label schema, and we will annotate a pilot batch so you can measure the quality before you commit.