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AI Data Solutions

Data Annotation

High-Quality Training Data to Scale AI Model Development

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Power Leading AI Model Development with
High-Quality Annotated Training Data.

Trust Innodata's subject matter experts to deliver accurate, reliable, and domain-specific multimodal data annotation, supporting use cases from search relevance and agentic AI to content moderation and beyond.

Image, Video, + Sensor Data Annotation

From faces to places, fuel your visual-based and computer vision models with high-quality annotated data.

Popular Use Cases:
  • Autonomous Vehicle LiDAR
  • Robotics
  • Anomaly Detection
  • Product Identification
  • Facial Recognition
  • Object Detection
  • And More...

Text, Document, + Code Data Annotation

Train your models with high-quality data annotated from the most complex text, code, and document sources.

Popular Use Cases:
  • Agentic AI Training
  • Search Relevance
  • Recommendation Engines
  • Natural Language Generation
  • Multilingual Translation
  • Entity + Relationships
  • And More...

Speech + Audio
Data Annotation

Scale your AI models and ensure model flexibility with diverse annotated speech and audio data.

Popular Use Cases:
  • Virtual Assistants
  • Multilingual Transcriptions
  • Speech-to-Text
  • Audio Classification
  • Regional Identification
  • Intent Capture
  • And More...

Our Data Annotation Process.

Our data annotation process is designed to deliver accurate, high-quality datasets tailored to your AI model training needs.

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  • Taxonomy Creation
    We define a clear and precise structure to organize and categorize your data effectively.
  • Guidline Development
    Detailed guidelines are crafted to ensure consistency and accuracy across annotations.
  • Pilot Execution + Delivery
    A potential pilot run validates the approach and aligns outputs with your project goals.
  • Project Kickoff
    The project officially launches with dedicated team members and defined milestones.
  • Single/Multi-Pass Annotation
    Data is annotated with one or multiple review passes to meet quality standards.
  • Quality Testing + Analysis
    Testing and analysis can be performed to guarantee the reliability and accuracy of the final dataset(s).

With our high-quality data labeling approach, you can trust Innodata’s annotated data to drive impactful and reliable AI training.

Why Choose Innodata for Data Annotation?

Bringing world-class data labeling services, backed by our proven history and reputation.

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Global Delivery Locations +
Language Capabilities

Innodata operates global delivery centers proficient in over 85 native languages and dialects, ensuring comprehensive language coverage for your projects.

High-Quality Annotated Data for Advanced Use Cases

We deliver highly accurate annotated data across modalities for advanced use cases like agentic AI, search relevance, and more, backed by a reputation for agility, scalability, and customer-centricity.

Domain Expertise Across
Industries

With 5,000+ in-house SMEs covering all major domains from healthcare to finance to legal, Innodata offers expert domain-specific annotation, collection, fine-tuning, and more.

Quick Annotation Turnaround at Scale​

Our globally distributed teams guarantee swift delivery of high-quality results 24/7, leveraging industry-leading data quality practices across projects of any size and complexity, regardless of time zones.

Annotation Specialists

Our ontologists, linguists, annotators, QA specialists, and data scientists collaborates on building ontologies, creating guidelines, and performing annotations for leading model development.

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Enabling Domain-Specific
Data Annotation Across Industries.

Agritech or Agriculture

Crop Yield Prediction, Livestock Monitoring, Plant Disease Detection, Weed Detection and Management, Soil Moisture Monitoring, and More….

Energy, Oil, or Gas

Environmental Monitoring, Risk Management, Fault Detection and Management, Geological Analysis, and More…

Media or Social Media

Search Relevance, Content Moderation, Ad Placements, Agentic AI Training, Facial Recognition, Podcast Tagging, Recommendation Engines, Sentiment Analysis, Chatbots, and More…

Consumer Products or Retail

Product Categorization and Classification, Agentic AI Training, Inventory Management, Visual Search Engines, Customer Reviews, Search Relevance, Recommendation Engines, Customer Service Chatbots, and More…

Manufacturing, Transportation, or Logistics

Contract Review and Analysis, Legal Transcription, eDiscovery, Entity Recognition, Compliance Monitoring, and More…

Banking, Financials, or Fintech

Fraud Detection, Risk Assessment, Trading Algorithms, Agentic AI Training, Customer Sentiment Analysis, Regulatory Compliance, and More…

Legal or Law

Contract Review and Analysis, Legal Transcription, eDiscovery, Entity Recognition, Compliance Monitoring, and More…

Automotive or Autonomous Vehicles

In/Off-Street Object Detection, Lane Detection and Tracking, Anomaly Detection, Sensor Fusion, Semantic Segmentation, and More…

Aviation, Aerospace, or Defense

Predictive Maintenance, Aircraft Detection, Air Traffic Control, Autonomous Systems Development, Geospatial Analysis, and More…

Healthcare or Pharmaceuticals

Medical Image Annotation, Drug Development, Health Record Annotation, Agentic AI Training, Pharmacovigilance, Medical Journal Annotation, and More…

Insurance or Insurtech

Underwriting Analysis, Claims Fraud Detection, Subject Risk Assessment, Customer Sentiment, Customer Service Chatbots, and More…

Software or Technology

Computer Vision Initiatives, Agentic AI Training, Audio and Speech Recognition, LLM Model Development, Image and Object Recognition, Search Relevance, Sentiment Analysis, Fraud Detection, and More...

8 out of 10 AI projects fail, with 96% of organizations facing challenges related to data quality, data labeling, and building model confidence.*

* Dimensional Research, 2019

Despite advancements in automation, human expertise remains indispensable, especially in ensuring high-quality data labeling.

Human annotators provide critical contextual understanding, ensure quality control, mitigate bias, and offer adaptability —elements that automation alone cannot fully address.

Why Humans Still Matter in Data Labeling.

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Looking for a Platform-Based Annotation Tool?

Enable your teams to label data at scale with our web-based annotation platform for record classification, document classification, inline classification, and image annotation.

Explore Our Annotation Platform

CASE STUDIES

Data Annotation Success Stories

See how top companies are transforming their AI initiatives with Innodata’s comprehensive data annotation solutions. Ready to be our next success story?

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Multilingual Content Moderation for Global Social Media Platform

Photography Platform Puts Context in Focus

Video Annotation to Automate Global Supply Chains

Scaling Product Categorization for an Online Supplier Discovery Platform

Building a Searchable Video Repository for a Global Non-Profit

Moderation of Advertisements

Multi-Object Tracking Improves Safety + Efficiency in Aircraft Landings

Winter Road Safety Innovation: Enhancing Collision Avoidance with LiDAR Data Annotation

Global Insurance Services Firm Builds AI Capability for Financial Data Assessment

Life Science Data Provider Acquires Right Annotated Data for Drug Search + Discovery

Moderation of Online Shop Listings

Conversational AI for Banking

Global Podcast Company Requires Intelligent Ad Insertion

Enhancing Autonomous Robot Navigation with Precise LiDAR Data Annotation

Articles + News

What is the Role of a Subject Matter Expert (SME) in AI? 

Read More

What are Golden Datasets in AI?

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What is Data Labeling for Machine Learning?

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LLM Jailbreaking Taxonomy

Read More

Innodata (NASDAQ: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. That’s why we’re on a mission to help the world’s leading technology companies and enterprises drive Generative AI / AI innovation. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 35+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

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Success Stories

Multilingual Content Moderation for Global Social Media Platform.

  • Innodata Case Study Challenge Icon Challenge

  • A top social media platform needed to improve AI models for various tasks, including search query relevance, ad review and placement, sentiment analysis, toxicity detection, and content moderation

  • The platform required annotated data for these use cases in multiple languages to ensure a global reach.

  • With multiple projects spanning different departments, the company needed a fast, scalable solution to deploy across various geographies and linguistic requirements.

  • Innodata Case Study Solution Icon Solution

  • A top social media platform needed to improve AI models for various tasks, including search query relevance, ad review and placement, sentiment analysis, toxicity detection, and content moderation

  • The platform required annotated data for these use cases in multiple languages to ensure a global reach.

  • With multiple projects spanning different departments, the company needed a fast, scalable solution to deploy across various geographies and linguistic requirements.

  • Innodata Case Study Impact Icon Impact

  • Innodata’s solution enabled the platform to refine its AI models, improving user engagement by providing more relevant content and maximizing ad revenue through better ad placement.

  • The comprehensive, multilingual content moderation also helped the platform foster a safer and more trustworthy environment for its global user base.

  • By delivering 100% accurate ground truth data, Innodata accelerated the company’s data-driven initiatives, supporting key areas like product development and brand protection worldwide.

Success Stories

Photography Platform Puts Context in Focus.

  • Innodata Case Study Challenge Icon Challenge

  • A leading AI-driven photography platform needed to enhance its ability to deliver curated photo albums based on who appears in the images.

  • Photographers regularly upload vast batches of event photos, which the platform matches with selfies provided by end users.

  • However, the platform’s model accuracy hinges on identifying and verifying multiple attributes, such as facial features and contextual details, critical to model training and prediction success.

  • Use cases spanned high-volume events like concerts, corporate gatherings, weddings, and sports competitions, making precision essential for quality results.

  • Innodata Case Study Solution Icon Solution

  • Innodata deployed a highly flexible and scalable data annotation team, providing near real-time judgments on the accuracy of the model’s predictions.

  • Our annotators meticulously evaluated key features in each photo, deciding if the AI’s match was correct.

  • Each image was initially reviewed by two annotators, with disagreements sent to a third, highly experienced moderator for resolution.

  • Innodata’s continuous monitoring of arbitration rates helped reduce bias and ensured cost-efficiency, allowing the platform to maintain both high accuracy and operational flexibility.

  • Innodata Case Study Impact Icon Impact

  • Through Innodata’s detailed and swift annotation process, the photography platform is revolutionizing the photo-sharing experience by delivering more accurate, time-saving curation for photographers and end users alike.

  • Our solution enabled the platform to process 2.5 million images in just the first 20 weeks of the project.

  • With an impressive 99% model accuracy achieved, the platform continues to enhance user experience, setting new standards in automated photography curation.

Success Stories

Creating Health and Medical Dialogues Across 8+ Specialties

  • Innodata Case Study Challenge Icon Challenge

A leading medical publisher approached Innodata with a critical need. They required a comprehensive dataset of medical dialogues, spanning over 8 different specialties, to support advancements in medical knowledge retrieval and automation. This dataset would serve as the foundation for semantic enrichment – a process that enhances the understanding of medical information by computers.

The key requirements were:

  • Multi-Specialty Focus: Dialogues needed to cover a wide range of medical sub-specialties, exceeding 20 in total. 
  • Real-World Tone: The dialogues should mimic genuine conversations within medical settings, while referencing the client’s specific “clinical key” as a knowledge base.
  • Pre-Determined Topics: The client provided a list of medical and health areas to ensure the dialogues addressed relevant issues.
  • Exceptional Accuracy: Achieving 99% accuracy in the medical content of the conversations was paramount.
  • Innodata Case Study Solution Icon Solution

Innodata implemented a multi-step workflow to deliver a high-quality medical dialogue dataset:

Expert Actor Recruitment: Innodata assembled a team of actors with real-world medical experience, including nurses, medical doctors, and students. This ensured the dialogues reflected the appropriate level of expertise and communication style for each scenario. 

Content Development: Our medical writers crafted the dialogues based on the client’s provided topics and “clinical key” resources. Each conversation maintained a natural flow while adhering to strict medical accuracy.

Multi-Layer Review: The dialogues underwent a rigorous review process by medical professionals to guarantee factual correctness and adherence to the 99% accuracy benchmark.

  • Innodata Case Study Impact Icon Impact

By leveraging Innodata's expertise in medical content creation and actor recruitment, the client received a unique and valuable dataset:

Extensive Medical Coverage: The dataset encompassed dialogues across a broad spectrum of medical specialties, providing a robust foundation for various applications.. 

Realistic Interactions: The diverse cast of actors and natural dialogue style ensured the dataset accurately reflected real-world medical communication.

Highly Accurate Content: The 99% accuracy level guaranteed the dataset’s suitability for training AI models and enriching medical knowledge retrieval systems.

Success Stories

Scaling Product Categorization for an Online Supplier Discovery Platform.

  • Innodata Case Study Challenge Icon Challenge

  • An online platform for supplier discovery and product sourcing wanted to Increase the coverage of their product database while ensuring that the existing content is up to date.

  • The platform needed a partner to categorize products, create product descriptions and company profiles for target suppliers from various industry domains and include product headings based on their taxonomy to enhance searchability.

  • Innodata Case Study Solution Icon Solution

  • Innodata deployed a team of highly-trained engineers and subject matter experts (SMEs) with specialized knowledge across various industries.

  • Our team reviewed extensive product catalogs, carefully categorized products using the client’s taxonomy, and wrote tailored company profiles and product descriptions.

  • To maintain accuracy and relevance, the workflow included frequent update cycles for existing products, ensuring the content remained current and valuable.

  • Innodata Case Study Impact Icon Impact

  • Innodata’s scalable delivery model enabled the platform to rapidly expand its product database, contributing to continued business growth.

  • The platform is now a premier source of data for manufacturers and buyers of engineering products, offering improved searchability and a more comprehensive product range.

  • With over 500,000 supplier profiles and more than 6 million products available, the platform has become a critical resource for users worldwide.

Success Stories

Building a Searchable Video Repository for a Global Non-Profit.

  • Innodata Case Study Challenge Icon Challenge

  • A global non-profit organization sought to create a fully searchable, organized repository for its extensive video collection, which included over 36,000 minutes of footage.

  • The collection featured diverse content, such as event highlights, institutional videos, social media clips, report launches, full event interviews, and media briefings.

  • With such varied and expansive content, the challenge was to develop a streamlined system that would allow for easy searchability and retrieval across multiple formats.

  • Innodata Case Study Solution Icon Solution

  • Innodata partnered with the non-profit to design a customized workflow and metadata schema tailored to their unique video collection.

  • After conducting an in-depth review of the various content types, Innodata implemented a web-based asset management portal for video ingestion and tagging.

  • Using this portal, each video was meticulously tagged according to a standardized schema, ensuring that all assets, regardless of content type, received a common set of metadata tags, making them easier to search and classify.

  • Innodata Case Study Impact Icon Impact

  • Innodata’s solution allowed the non-profit to efficiently organize its vast video library, transforming it into a fully searchable and accessible resource.

  • The streamlined tagging process significantly enhanced the institution’s ability to search for and retrieve specific videos, enabling better utilization of content for reports, events, and social media engagement.

  • The new system optimized their video management process and unlocked greater value from their extensive content.

Success Stories

Moderation of Advertisements.

  • Innodata Case Study Challenge Icon Challenge

  • A global measurement and analytics company needed a comprehensive content moderation program to ensure the safety and appropriateness of advertisements across over 35 international markets.

  • As the company provides content ratings for various media, maintaining brand safety in diverse regions required the ability to monitor and assess vast amounts of video content for compliance with cultural and market-specific standards.

  • Innodata Case Study Solution Icon Solution

  • Innodata implemented a robust video content moderation program, processing approximately 11,000 hours of video annually to ensure advertisements meet strict brand safety guidelines.

  • The project was initially staffed with subject matter experts and moderators in the Middle East and Indonesia, ensuring both cultural awareness and precision in moderation.

  • With plans to expand into additional countries, Innodata’s scalable solution was designed to support the client’s growing needs across all markets.

  • Innodata Case Study Impact Icon Impact

  • Through Innodata’s moderation program, the client achieved consistent, high-quality ad moderation that ensured brand safety across multiple regions.

  • By streamlining the review process, Innodata helped the client protect brand integrity and adhere to regional content regulations, fostering trust between advertisers and consumers.

  • The program’s scalability also positioned the client to expand its content moderation capabilities as its global reach grows.

Success Stories

Multi-Object Tracking Improves Safety + Efficiency in Aircraft Landings.

  • Innodata Case Study Challenge Icon Challenge

  • A leading aerospace manufacturer is focusing its efforts is on the use of multi-object tracking (MOT) technology. MOT is a computer vision technique that can track multiple objects in a video sequence.

  • For this project MOT is being used to track aircraft, ground vehicles, people, and animals in the vicinity of airport runways during the approach and landing phases of flight.

  • The use of MOT has several potential benefits including improved safety by identifying potential hazards on the runway, such as vehicles or people who have strayed into restricted areas. Second, MOT can help to improve efficiency by optimizing the use of runway space and reducing the time it takes for aircraft to land and take off.

  • Innodata Case Study Solution Icon Solution

Innodata was enlisted by the company as a trusted vendor to perform data annotation services that assist in the following tracking on-site from the videos the aircraft videos return:

  • Aircraft: Position and movement for safe separation.
    Ground vehicles: (e.g., service trucks) to prevent collisions with aircraft.
  • People: To prevent unauthorized access and ensure personnel safety.
  • Animals: (e.g., birds) to avoid bird strikes and other incidents.

Innodata processes approximately 220 videos monthly.

  • Innodata Case Study Impact Icon Impact

  • This program is still in the early stages of using MOT, but the company is confident that this technology has the potential to significantly improve the safety and efficiency of aircraft landings.

  • The company has retained Innodata for ongoing support on this initiative.

Success Stories

Winter Road Safety Innovation: Enhancing Collision Avoidance with LiDAR Data Annotation.

  • Innodata Case Study Challenge Icon Challenge

  • In regions like Canada and Alaska, harsh winter conditions—such as snow-covered, icy roads and poor visibility—contribute to numerous accidents each year.

  • A leading technology university sought to address this issue by partnering with Innodata for an AI research project aimed at collision avoidance and road disaster management.

  • The project required accurate annotation and synchronization of 3D LiDAR data, sensor fusion, and image classification to build a robust AI model.

  • The dataset included 12 main vehicle classes and over 60 subclasses, with each frame containing 20-40 vehicles.

  • Additional challenges involved poor-quality imagery due to snow and sleet, and issues with aligning LiDAR and camera data timestamps.

  • Innodata Case Study Solution Icon Solution

  • In regions like Canada and Alaska, harsh winter conditions—such as snow-covered, icy roads and poor visibility—contribute to numerous accidents each year.

  • A leading technology university sought to address this issue by partnering with Innodata for an AI research project aimed at collision avoidance and road disaster management.

  • The project required accurate annotation and synchronization of 3D LiDAR data, sensor fusion, and image classification to build a robust AI model.

  • The dataset included 12 main vehicle classes and over 60 subclasses, with each frame containing 20-40 vehicles.

  • Additional challenges involved poor-quality imagery due to snow and sleet, and issues with aligning LiDAR and camera data timestamps.

  • Innodata Case Study Impact Icon Impact

  • The project delivered a high-quality annotated dataset, which enabled the university to train an advanced AI model for collision avoidance and road hazard detection.

  • The model significantly improved public safety by providing real-time alerts for accident management. Additionally, the project was completed on time and within budget, allowing the university to implement their AI model during the winter season, making immediate contributions to improving road safety in snow-prone regions.

Success Stories

Global Insurance Services Firm Builds AI Capability for Financial Data Assessment.

  • Innodata Case Study Challenge Icon Challenge

  • A global insurance services company needed datasets to train its AI platform for identifying critical events in SEC filings and earnings call transcripts.

  • The company’s platform enables the assessment of company data from financial documents. This allows the company to perform automatic corporate risk assessment and evaluation of companies.

  • Innodata Case Study Solution Icon Solution

  • Innodata labeled hundreds of SEC filings and earnings call transcripts to identify finance-related events in the text of the documents.

  • Labeling of the documents utilized a taxonomy provided by the insurance company.

  • Innodata deployed subject matter experts and implemented an inter-annotator agreement process to ensure very high accuracy in the labeling and annotation of the documents.

  • Innodata Case Study Impact Icon Impact

  • Innodata created the datasets needed to train the AI platform to perform a highly accurate assessment of company data from financial documents.

  • Using its proprietary text annotation platform, Innodata was able to seamlessly implement the required annotation process and ensure the quality of the training datasets.

Success Stories

Life Science Data Provider Acquires Right Annotated Data for Drug Search + Discovery.

  • Innodata Case Study Challenge Icon Challenge

  • A leading life sciences company offering a scientific research discovery platform needed large-scale, high-quality annotated datasets to power predictive and prescriptive analytics for drug discovery and research funding.

  • The platform required accurate, structured data to improve the precision of its insights, enabling researchers to drive investments in drug development and prevent patent infringement.

  • The challenge was in sourcing and annotating vast volumes of unstructured scientific content.

  • Innodata Case Study Solution Icon Solution

  • To begin the process of creating high-quality labeled scientific datasets, Innodata’s annotation experts set up their platform to automate the process of entity extraction to pull out relevant keywords and references from the source documents.

  • Innodata’s experts then annotated millions of pages of scientific data, research, and articles. They created structured XML datasets that could be used to train the AI platform in predictive and prescriptive analytics.

  • Innodata Case Study Impact Icon Impact

  • The enriched datasets enabled the life sciences company to deliver actionable insights, helping their clients enhance drug research, secure research funding, and accelerate new drug development.

  • Furthermore, the enhanced data allowed the platform to help researchers identify patent risks early, reducing the likelihood of infringement.

  • By providing more accurate and structured data, Innodata’s solution drove faster, more effective decision-making across the customer’s scientific discovery processes.

Success Stories

Moderation of Online Shop Listings.

  • Innodata Case Study Challenge Icon Challenge

  • A major online shopping platform faced the challenge of monitoring and reviewing user-submitted listings to prevent the sale of counterfeit products and fraudulent items.

  • With thousands of listings uploaded daily, the platform needed a robust solution to quickly detect and remove replica products and scams, maintaining a trustworthy marketplace for its users.

  • Innodata Case Study Solution Icon Solution

  • Innodata deployed a team of experienced content moderators to review and identify replica and fraudulent marketplace listings.

  • Innodata’s content moderation experts review the listings using the client’s online platforms as soon as the listings are posted by users.

  • The Innodata team works as an extension of the client, where the client defines the working days and shifts, the times covered in the shifts, and the number of fulltime content moderators assigned per shift.

  • Innodata Case Study Impact Icon Impact

  • Innodata’s solution enabled the client to maintain a safe and secure shopping environment by promptly identifying and removing fraudulent and replica listings.

  • This real-time moderation not only enhanced user trust but also protected legitimate sellers and buyers from counterfeit activities.

  • The scalable and flexible setup allowed the client to manage large volumes of listings while maintaining quality control.

Success Stories

Conversational AI for Banking.

  • Innodata Case Study Challenge Icon Challenge

  • One of Canada’s largest commercial banks sought a flexible data annotation partner to help with a variety of one-time annotation projects that each utilize different models and data.

  • While the projects vary, they are always banking related, which means high accuracy, security, and compliance are paramount to success.

  • Innodata Case Study Solution Icon Solution

  • Innodata leveraged its AI-enabled platform and in-house subject matter experts to complete a range of projects, including classification of verbatims using a multilabel model, paraphrasing to create a training corpus for a conversational agent and validations of category classifications.

  • Each project uses double pass annotation with arbitration to ensure that the accuracy goals are always met.

  • Innodata Case Study Impact Icon Impact

  • Innodata’s solution helped the bank enhance and accelerate the ground truth pipeline used to train their conversational models.

  • Training on precise and relevant utterances helped the chatbot to become more articulate, more responsive and increase customer satisfaction.

Success Stories

Global Podcast Company Requires Intelligent Ad Insertion.

  • Innodata Case Study Challenge Icon Challenge

  • A global podcast company offering hosting, analytics, and ad marketing services needed a way to automatically insert ads at optimal points in podcasts, such as during transitions or segment changes.

  • This process, known as “dynamic ad insertion,” required the ads to be seamlessly stitched into raw audio files as they were requested for download.

  • To achieve this, the company needed a high-quality training dataset to teach its platform to identify the best ad insertion points based on podcast structure.
  • Innodata Case Study Solution Icon Solution

  • Innodata evaluated ad placements across thousands of podcasts, analyzing each for suitable insertion points.

  • Our team provided comprehensive evaluation results, detailing labels that indicated the quality of each ad placement and explaining the reasoning behind the labels.

  • To ensure the highest level of accuracy, Innodata employed a double-pass blind process, where two annotators reviewed each podcast separately, and a senior annotator arbitrated any disagreements.

  • Innodata Case Study Impact Icon Impact

  • Innodata delivered highly accurate training datasets, enabling the company to significantly improve its AI-powered ad insertion system.

  • With this refined dataset, the platform can now automatically and intelligently insert ads at ideal locations, enhancing the listener experience while maximizing ad engagement and revenue.

  • The improved ad placement also allows for better monetization opportunities across their vast podcast library.

Success Stories

Enhancing Autonomous Robot Navigation with Precise LiDAR Data Annotation.

  • Innodata Case Study Challenge Icon Challenge

  • A prominent manufacturer of high-end autonomous cleaning robots was struggling with increasing customer complaints about their robots colliding with obstacles.

  • These collisions were causing costly repairs and replacements, tarnishing the company’s brand reputation, and resulting in significant monthly revenue losses.

  • The company lacked an in-house engineering team capable of scaling the necessary data annotation process for their robots’ LiDAR sensor data.

  • They sought a solution to provide accurate ground truth data to help retrain the robots for improved object recognition and collision avoidance.

  • Innodata Case Study Solution Icon Solution

  • Innodata partnered closely with the client to analyze their existing datasets and address the primary challenge: the robots needed precise segmentation to distinguish between the floor and a wide variety of obstacles found in different home and office environments.

  • To tackle this, Innodata established a dedicated annotation team and ran pilot projects to align the workflow with the client’s specific requirements.

  • Through extensive LiDAR data annotation, Innodata helped the client retrain their robots, ensuring that the object recognition models were accurately identifying and navigating around obstacles.

  • Innodata Case Study Impact Icon Impact

  • Thanks to the high-quality annotation work provided by Innodata, the client successfully retrained their robots, leading to a dramatic improvement in their ability to maneuver around obstacles.

  • The result was a significant reduction in customer complaints, repair costs, and revenue losses. By preventing further damage to the robots, the company was able to restore customer confidence, enhance product reliability, and reduce the need for expensive support services.