Services · Data Annotation
Data Annotation Services in India
Image, video, text and audio labeling, RLHF and AI evaluation by trained annotators, linguists and raters. Outsource a managed project or extend your team with dedicated annotators, with QA built into every batch.
- Image, video, text & audio
- RLHF & AI evaluation
- Outsourcing or dedicated teams
AI-Ready Data Services
Human Expertise Behind Your Training Data
Every AI model inherits the strengths and the blind spots of its training data. Zilo AI’s data annotation services give AI teams trained people and structured workflows for image and video annotation, audio transcription, text and multilingual labeling, RLHF and AI evaluation, backed by 3000+ trained text annotators, 2500+ trained image annotators and 2000+ language experts across 22 languages.
Work with us the way your pipeline needs. Hand over a complete project and receive QA-checked datasets in your format, or add dedicated annotators, raters and AI trainers who work inside your tools and processes. Either way, every engagement starts with a calibrated pilot, runs with multi-level review and gives you a single point of contact from the first batch to ongoing delivery.
Every Data Type
Images, video, LiDAR, documents, text, audio and LLM conversations, labeled to your taxonomy and format.
Trained Teams
Annotators, linguists, raters and AI trainers who learn your guidelines before they label production data.
Built-In QA
Pilot batches, calibration, multi-level review and gold-standard checks keep quality steady as volumes grow.
Outsource or Extend
Hand us a managed annotation project or hire dedicated annotators to extend your in-house data team.
What We Annotate
Five Annotation Services, One Data Partner
Open any service to see the annotation types, tools and specialist roles involved. Each one can run as a managed project or be staffed with dedicated annotators.
Image & Video Annotation
Bounding boxes, segmentation, keypoints, object tracking and 3D LiDAR labeling for computer vision, by trained annotators.
6 roles
Detection, segmentation and tracking models need labels that stay consistent from the first frame to the last. Trained image and video annotators draw bounding boxes, polygons and masks, place keypoints, track objects across frames and fit 3D cuboids to LiDAR point clouds. They work to your class taxonomy in your own tool or an open-source platform such as CVAT, with pilot calibration and multi-level review before delivery in COCO, YOLO or your schema.
Image Data Annotator
Labels still images to your class taxonomy, from product photos and scanned documents to retail shelves, aerial views and street scenes, applying boxes, image-level tags and attributes under consistent edge-case rules.
Video Data Annotator
Annotates video at clip, segment and frame level, labeling objects frame by frame, marking actions, events and scene changes with timestamps and selecting keyframes, so temporal models learn what happens and when.
Bounding Box, Polygon & Segmentation
Draws tight 2D boxes, detailed polygons and pixel-level semantic, instance or panoptic masks wherever shape matters, from vehicles and pedestrians to crop rows, product packaging and surface defects.
Keypoint & Pose Annotation
Places skeletal keypoints and landmarks on people, hands, faces and objects, with visibility flags for occluded points, to train pose estimation, gesture recognition, sports analytics and ergonomics models.
Video Object Tracking
Follows every object across frames with persistent track IDs, re-identifies objects after occlusion and reviews interpolated boxes, producing clean trajectories for multi-object tracking, traffic and retail analytics.
3D Point Cloud / LiDAR Annotation
Fits 3D cuboids and point-level labels to LiDAR and radar point clouds, tracks objects across sweeps and links them to camera frames for autonomous driving, robotics, drone and mapping datasets.
Audio & Speech Annotation
ASR transcription, speaker diarization, timestamps and audio event tagging, with multi-language and dialect coverage.
6 roles
For ASR engines, voice assistants and speech analytics, raw recordings become training-ready data. ASR transcriptionists and audio annotators deliver verbatim or clean-verbatim transcripts, speaker diarization, word-level timestamps and sound-event or emotion tags across 22 languages, following your style guide. Every batch is reviewed against the audio before export to JSON, SRT, WebVTT, TextGrid or RTTM.
Voice / Audio Data Annotator
Listens to audio clips and labels speech and non-speech content: segmenting utterances, marking noise, music and silence, and tagging speaker attributes, language, intent or sentiment according to your taxonomy.
ASR Transcriptionist
Produces verbatim or clean-verbatim transcripts for ASR training and evaluation, applying your rules for fillers, false starts, numbers, acronyms, code-switching and unclear speech, with utterance- or word-level timestamps.
Speaker Diarization & Timestamping
Marks who spoke when in multi-speaker audio such as meetings, interviews and support calls, handles overlapping speech and short interjections, and aligns segment boundaries tightly to the waveform.
Audio Event & Emotion Tagging
Labels sound events such as alarms, sirens, laughter, applause and background noise, and rates emotion, sentiment and speaking style for voice analytics, sound recognition and expressive speech models.
Multilingual & Dialect Transcription
Language experts transcribe speech across 22 languages, capturing regional accents, dialect vocabulary and code-mixed speech, and deliver native-script or romanized output with optional translation, as your project requires.
ASR & TTS Output Evaluation
Checks machine transcripts against the audio to correct errors and support word error rate analysis, and rates synthetic voices for naturalness, pronunciation and prosody in structured listening tests.
Text & Language Annotation
Entity, sentiment and intent labeling, document extraction and translation QA for NLP models, in 22 languages.
6 roles
Chats, reviews, support tickets, contracts and search queries all carry meaning your NLP models must learn to read. Linguist annotators and NLP data labelers mark entities and relations, sentiment and intent, and document fields, while bilingual reviewers score translation quality. Multilingual annotators work across 22 languages, including code-mixed and transliterated text, and every label schema is tested on a pilot before scaling with consensus and gold-standard checks.
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.
RLHF & AI Trainer Services
SFT demonstrations, preference ranking, rubric grading and red-teaming by vetted AI trainers and domain experts.
5 roles
Post-training runs on human judgment. AI trainers write SFT demonstrations, compare and rank model responses, grade outputs against your rubric and red-team for unsafe behavior, with domain experts in coding, math, STEM and finance for tasks generalists cannot judge. Trainers are screened for reasoning and writing, calibrated on your rubric and reviewed through gold tasks and agreement checks.
AI Trainer / RLHF Specialist
Works across the post-training loop: writing and correcting responses, comparing model outputs, scoring helpfulness, honesty and harmlessness, and explaining each judgment clearly enough for researchers to act on.
SFT Demonstration Writer
Writes prompts and ideal responses for supervised fine-tuning that match your tone, format and policy rules, from step-by-step reasoning and structured outputs to tool calls and multi-turn conversations.
Preference Ranking & Rubric Grader
Compares two or more model responses, ranks them against your criteria, grades each dimension on a defined rubric and records concise rationales, producing preference data for reward models and DPO.
Domain Expert Trainer
Brings subject depth to tasks generalists cannot judge: writing and debugging code, solving multi-step math and STEM problems, checking financial reasoning and verifying facts, so your model learns from correct answers.
LLM Red-Teamer
Stress-tests models with adversarial prompts, jailbreak attempts, prompt injections and sensitive edge cases, documents failures against your safety policy and helps build refusal and safety-tuning datasets.
Search Quality Rating & AI Evaluation
Search relevance rating, LLM response evaluation, AI safety moderation and annotation QA by trained human raters.
4 roles
Automated metrics show that a model changed; trained raters show whether it improved. Search quality raters judge results against query intent, AI response raters score LLM answers for helpfulness, accuracy and safety, content moderators apply your AI safety policy, and annotation QA specialists audit datasets from any team or vendor. Every program starts with guideline calibration, and ratings stay locale-aware for each market you serve.
Search Quality Rater / Search Engine Evaluator
Rates how well search results, ads, local listings and recommendations match the intent behind a query, judging relevance, page quality, freshness and locale fit according to detailed rating guidelines.
AI Response Rater / AI Quality Analyst
Evaluates chatbot and LLM responses for helpfulness, factual accuracy, instruction following, tone and safety, compares model versions side by side and turns rating patterns into clear quality insights.
Content Moderator (AI Safety)
Reviews user prompts and AI-generated text and images against your safety policy, labels harmful, biased or sensitive content, and builds the classifier and safety-tuning data that keeps model behavior within policy.
Annotation QA Specialist
Audits labeled data from in-house teams or other vendors: sampling batches, measuring agreement, categorizing errors and feeding findings back into guidelines, so every dataset meets your acceptance criteria before training.
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 — 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
Data Annotation Services: Common Questions
Can’t find the answer you’re looking for? Our team is happy to help — reach out and we’ll respond quickly.
Talk to Our Team
- +91 77604 02792
- connect@ziloservices.com
- Mon–Fri: 8am – 10pm
- Hebbal Kempapura, Bangalore
What data annotation services does Zilo AI provide?
Our data annotation services span five areas: image and video annotation including LiDAR, audio and speech annotation, text and multilingual NLP labeling, RLHF and AI trainer support for LLMs, and human evaluation such as search quality rating, AI response rating, AI safety moderation and annotation QA. Each can run as a managed project or with dedicated annotators.
Should we outsource annotation or hire dedicated annotators?
Outsource when you want finished, QA-checked datasets without managing people: we handle staffing, training, review and delivery. Hire dedicated annotators when labeling is continuous and closely tied to your team, so they work in your tools under your direction. If you are unsure, a managed pilot batch is a low-commitment way to compare.
How do you keep annotation quality consistent at scale?
Quality is built into the workflow rather than checked at the end. A pilot batch tests your guidelines, calibration sessions settle edge cases with your team, and production runs with multi-level review, gold-standard and consensus checks and QA sampling before delivery. Recurring errors go back into annotator training, and guidelines evolve with your data.
Do you annotate LiDAR, 3D point cloud and video data?
Yes. Our vision teams fit 3D cuboids and point-level labels to LiDAR and radar point clouds, keep object IDs consistent across sweeps and link them to camera frames for sensor fusion. For video, they track objects across frames and label actions and events with timestamps, exporting to KITTI, COCO or your own schema.
Which transcription styles and formats do you support for ASR data?
ASR transcriptionists work in verbatim, clean-verbatim or normalized styles, following your rules for fillers, numbers and non-speech tags, with speaker diarization and utterance- or word-level timestamps. Deliverables include JSON or JSONL, SRT and WebVTT captions, Praat TextGrid, ELAN EAF and RTTM files, or a custom schema agreed during the pilot.
Can your AI trainers support RLHF and LLM fine-tuning?
Yes. AI trainers write SFT demonstrations, rank and compare model responses, grade outputs against your rubric and red-team for unsafe behavior. For coding, math, STEM and finance tasks we bring in domain expert trainers, and every trainer is calibrated on your rubric and reviewed through gold tasks before and during production.
What do your search quality raters and AI response raters evaluate?
Search quality raters judge whether results, ads and recommendations match the intent behind a query, including page quality and locale fit. AI response raters score chatbot and LLM answers for helpfulness, accuracy, instruction following and safety, often side by side across model versions, and turn rating patterns into insights your team can act on.
Can you handle multilingual annotation projects?
Yes. With 2000+ language experts across 22 languages, including German, French, Spanish, Arabic, Mandarin, Korean and Vietnamese, we support multilingual text, speech and evaluation work. Annotators are matched to both the language and the subject domain, so labels reflect local meaning, idiom and intent rather than literal translation.
How do we start a data annotation project with Zilo AI?
Share sample data, your label taxonomy or guidelines, expected volumes and preferred output format. We scope a pilot batch, which can usually begin within a few working days, agree on quality checks and turnaround with you, and scale the team once the pilot output meets your expectations. Your data stays in secure, access-controlled workflows aligned to your data-handling requirements.
Can Zilo AI also help us hire AI engineers?
Yes. Alongside annotation, Zilo AI provides IT staffing services for AI engineers, data scientists and cloud data engineers, so the people who build your models and the data they learn from can come from one partner.
Ready to Build AI-Ready Data?
Tell us about your data, labels and volumes. We will propose a pilot batch, a quality plan and a team setup matched to your model and timeline.