AI Data Annotation Services
Domain-Expert Data Annotation & Labeling for AI
Image, video, text, document, speech, and sensor data labeled by trained subject-matter experts across 120+ languages, supporting advanced AI and ML programs from search relevance and agentic AI to content moderation and autonomous systems.
- Specialist annotators (linguists, ontologists, clinicians, engineers), not crowd labor
- Proprietary workflow technology with human review built into every stage
- One partner from scoped pilot through full-scale production
Years of Experience
Subject Matter Experts
Languages & Dialects
Trusted By
of the Magnificent Seven
What We Annotate
Every Modality Your Models Train On
One partner across image, text, audio, and sensor data, with consistent quality standards and delivery governance across modalities.
Image, Video + Sensor
Bounding boxes, semantic segmentation, keypoints, and LiDAR point-cloud labeling for computer vision and physical AI.
- Object Detection
- Agentic AI Training
- Autonomous Systems
- Robotics
Text, Document + Code
Entity and relationship extraction, intent, sentiment, and preference data drawn from your most complex sources.
- Search Relevance
- Anomaly Detection
- Recommendations
- Document Intelligence
Speech + Audio
Transcription, diarization, intent capture, and audio classification across accent-diverse, multilingual programs.
- Speech-to-Text
- Virtual Assistants
- Diarization
- Intent Capture
Why Innodata
Built for Complex Annotation Programs, Not Commodity Labeling
Accuracy at volume comes from who does the work and how the work is engineered.
01
Domain Expertise, Not Crowd Labor
Linguists, ontologists, QA specialists, data scientists, and trained industry experts annotate work in fields they already understand. Judgment calls get made by people qualified to make them.
02
Quality Engineered Into the Workflow
Structured guidelines, multi-stage QA, sampling, reviewer feedback, and arbitration are part of the process design. Quality is measured continuously, not inspected at the end.
03
Enterprise-Scale Delivery Infrastructure
Global delivery operations built to run complex, high-volume, multilingual programs concurrently, with the staffing and management depth to absorb changes mid-program.
04
Configurable to Your Program
Datasets, modalities, workflows, taxonomies, and QA requirements are configured to fit your program. You are not forced into one rigid process built for someone else.
Repeatable AI data workflows, configured per program without custom development.
Technology-Enabled Delivery
Proprietary Technology. Human Expertise Where It Matters.
Our annotation programs run on Innodata’s proprietary workflow technology. It handles orchestration and repetitive operational work, so our experts handle judgment: domain interpretation, annotation, quality validation, and edge cases.
- Configurable Workflows and Task Routing
- Taxonomy and Scoring Rubric Management
- QA Sampling and Human Review
- Reviewer Feedback Loops
- Dataset History and Auditability
We could not have developed the scale of our classifiers without Innodata. I’m unaware of any other partner that could have delivered with the speed, volume, accuracy, and flexibility we needed.
Program Manager, AI Research Team
Magnificent Seven Technology Company
How Engagements Run
Four Stages From Scope to Production Data
Run by a named delivery lead, with quality gates you can inspect at every stage.
Scope + Design
Define data, taxonomy, guidelines, edge cases, quality requirements, languages, and delivery targets.
Pilot
Run a scoped dataset to validate the workflow and the quality approach before you commit.
Scale
Launch the dedicated team and annotation workflow against agreed throughput targets.
QA + Delivery
Review, validate, correct, and deliver production-ready annotated datasets on schedule.
Data Annotation Use Cases
Training Data for Advanced AI Systems
From foundation models and intelligent agents to computer vision and speech AI, Innodata builds domain-specific annotated datasets for complex model training, evaluation, and production use cases.
Agentic AI + LLM Training
Instruction data, preference data, prompt-response labeling, and evaluation datasets.
Search Relevance + Recommendations
Query-document relevance, ranking judgments, product relevance, and recommendation signals.
Computer Vision
Object detection, semantic segmentation, keypoints, image classification, and visual understanding.
Physical AI + Autonomous Systems
LiDAR, sensor fusion, robotics, spatial understanding, and anomaly detection.
Document Intelligence
Entity and relationship extraction, document classification, and structured information extraction.
Speech + Conversational AI
Transcription, diarization, intent capture, speech-to-text, and multilingual speech.
Content Moderation + Safety
Policy labeling, classification, safety review, and expert judgment on nuanced content.
Multilingual AI
Language-specific annotation, linguistic interpretation, multilingual classification, and region-specific data programs.
Selected Programs
Where Our Annotation Work Lands
Technology + GenAI
LLM and agentic AI training data, instruction and preference sets, model evaluation.
Automotive + Autonomous
LiDAR and sensor fusion, lane detection, semantic segmentation for physical AI.
Healthcare + Life Sciences
Medical image and record annotation, clinical document extraction, pharmacovigilance.
Financial Services
Fraud and risk signals, regulatory document intelligence, entity extraction.
Retail + Consumer
Product categorization, visual search, search relevance, and media tagging.
Media + Content
Content moderation, policy labeling, multilingual speech review and sentiment analysis.
FAQ
Common Questions About Data Annotation
What types of data can Innodata annotate?
We annotate every modality your models train on: image, video, and sensor data (bounding boxes, semantic segmentation, keypoints, LiDAR point clouds); text, documents, and code (entity and relationship extraction, intent, sentiment, and preference data); and speech and audio (transcription, diarization, intent capture, and audio classification). Programs can span a single modality or combine multimodal datasets across 120+ languages.
How does Innodata ensure data annotation quality?
Quality is engineered into the workflow rather than inspected at the end. Every program runs on structured annotation guidelines with multi-stage QA, sampling, reviewer feedback loops, and arbitration built into the process design. Quality is measured continuously against agreed requirements, and domain experts make the judgment calls on ambiguous or edge-case data.
Can Innodata support domain-specific data annotation?
Yes. Annotation is performed by trained subject-matter experts — linguists, ontologists, clinicians, engineers, QA specialists, and data scientists — who work in fields they already understand, rather than generic crowd labor. This is what allows us to support specialized programs in areas like healthcare, financial services, and autonomous systems.
Can Innodata support high-volume and multilingual annotation programs?
Yes. Our global delivery operations are built to run complex, high-volume, multilingual programs concurrently, with annotation support across 120+ languages and the staffing and management depth to absorb changes mid-program.
How does Innodata combine technology with human annotation?
Programs run on Innodata’s proprietary workflow technology, which handles orchestration and repetitive operational work — task routing, taxonomy and scoring rubric management, QA sampling, and dataset history and auditability. That frees our human experts to focus on judgment: domain interpretation, annotation, quality validation, and edge cases.
How does a data annotation engagement begin?
Engagements run in four stages, led by a named delivery lead. In Scope + Design, we define the data, taxonomy, guidelines, edge cases, quality requirements, languages, and delivery targets. A scoped Pilot validates the workflow and quality approach before you commit. We then Scale a dedicated team against agreed throughput targets, and QA + Delivery ensures production-ready annotated datasets arrive on schedule.
Get Started
Tell Us About Your Annotation Program
Share your data type, volume, languages, quality requirements, and turnaround goals. Our team will connect you with the right data annotation experts.