Solutions
Data Annotation
With 4,000+ subject matter experts and production-grade platforms, no job is too big or complex
Featured Article
Five Questions to Ask Before Getting Started with Data Annotation
High Quality Training Data to Accelerate AI and Machine Learning
Highest-quality annotation of text, images, audio and video data for complex models. Ideal for computer vision, sentiment analysis, entity linking, text categorization, and syntactic parsing and tagging models.
High Accuracy
World-class workflows and easy-to-use interfaces coupled with process control techniques developed over 30 years building the world’s highest quality data products.
Domain Experts
No crowdsourcing - only accredited experts. Internal, certified SMEs employed for projects in healthcare, law, sciences and finance.
Efficient, Fast Results
Jump-start efforts with transferred learning using our out-of-the-box multiclass ML Catalyst™ models.
Data Security
We’re certified to handle the most sensitive and highly regulated data.
Data Annotation Services
From complex documents to vital images, our experts precisely tag the data you need to train your models.
Image & Video
From faces to places, power your computer vision models with high-quality image data.
Text
Speech & Audio
Scale your AI models & ensure model flexibility with diverse speech/audio data.
Various Languages | Dialects | Dialogue Types | Scenarios/Environments
Content Moderation
Identify & classify inappropriate content so you can do business with confidence.
Conversational AI
The right datasets to fuel training data for chatbots and virtual assistants.
Conversational Agents | Intents & Utterances for Chatbots | Transcription | Content Summarization
Deep Expertise Across Diverse Data Formats & Industries
Innodata specializes in data annotation of all types of documents & formats for any industry – no matter how complex
Contracts
ISDA | GMRA | MRA | MSFTA | MSLA
Legal Data
Legislation | Regulatory | Case Law | SEC | International Tax Treaties
Financial Reports
Investor Presentations | Earnings Calls | SEC Documents
Patent Data
Scientific | Chemicals | Drugs | Engineering
Scientific Data
Journals | Abstracts | Conference Proceedings
Medical Records
Pharmacovigilance | Adverse Drug Events | Product Labels
Image & Video
Facial Recognition | Satellite Imagery | Aerial Photography
Audio
Transcriptions | Sound Clips | Broadcasts | Intent & Utterances
Insurance Claims
Property & Casualty | Life | Medical | Assets
Getting Started is Simple
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Data Annotation In Action
Success Stories
Learn how we’re helping our clients secure the ground truth they need to succeed.
Scientific Publisher Finds the Right Formula for Creating Structured Labeled Data
Scientific Publisher Finds the Right Formula For Creating Structured Labeled Data
Objective
A leading scientific information provider required labeled data for content related to drug discovery and research funding.
Solution
- Innodata created structured labeled data in XML from structured and unstructured data sources.
- Professional teams of bio-medical and chemistry experts labeled millions of pages of scientific patent information and created structured data for predictive and prescriptive analytics platforms.
- We used advanced entity extraction, text and data mining, augmented by professional scientific SMEs to create high-quality labeled scientific datasets.
Results
- Innodata provided comprehensive information that has helped the client build and maintain reliable, up-to-date information of global patterns that matter.
- The patent analytics platform is used by leading global drug and chemical manufacturers in efforts to avoid patent infringement, identify potential investments in new drug development and research fund attribution.
Bio-Medical & Chemistry SMEs Meticulously Annotate Global Patent Data
Bio-Medical & Chemistry SMEs Meticulously Annotate Global Patent Data
Objective
Post-market adverse drug reaction surveillance is a mandatory regulatory responsibility of pharmaceutical companies. The adverse reaction data is hidden in the scientific content published in research journals.
Thus, a leading pharmacovigilance platform required labeled datasets for training AI models based on this information in order to help its clients improve their drug safety workflow processes.
Solution
- Innodata setup a team of bio-medical subject matter experts to label unstructured data; the adverse drug reactions are published across various journals and written as scientific research abstracts.
- Innodata’s medical experts annotated the abstracts from scientific journals broken into individual words with a customized labeling application.
- The SMEs studied the abstracts and labeled individual words to a controlled ontology of medical terms to create an annotated dataset for machine learning.
Results
- Innodata’s labeled datasets have helped the company build machine learning models that predict adverse drug events with high accuracy and enables pharma companies to navigate the increasingly complex and ever-changing regulatory environment.
- Today, our client can confidently offer its customers a fully compliant, configurable, easy-to-adopt and audit-ready solution for scientific literature monitoring and pharmacovigilance.
Leading Photography Platform Puts Context in Focus Through Annotation of Millions of Images
Leading Photography Platform Puts Context in Focus Through Annotation of Millions of Images
Objective
A leading AI photography platform required annotated data for photographs. In order to teach the tool to recognize “context” from its photos, the company needed large volumes of footage with detailed labeling, and required it within 24 hours.
Solution
- Innodata gathered a team of annotation resources, setup the infrastructure and trained the team in 4 weeks.
- An initial POC test was conducted with multiple vendors, Innodata ranked #1.
Results
- The client has an on-demand, flexible and scalable data annotation team on-call.
- Client has been able to meet the market demands effectively.
- To-date, Innodata has processed 2.5 million images in 20 weeks and delivered the batches in near real-time.
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