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Trusted Data Solutions for Powerful
Generative AI Model Development

Trusted Data Solutions for Powerful Generative AI Model Development

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Fuel Advanced AI/ML Model Development With
Data Solutions for Generative AI

High-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.

Fuel Advanced AI/ML
Model Development With
Data Solutions for Generative AI

High-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.

Data Collection & Creation

Curate or generate a wide range of high-quality datasets across data types and demographic categories in over 85 native languages.

Our global teams rapidly collect or create realistic and diverse training datasets tailored to your unique use case requirements to enrich the training of generative AI models.

Additionally, develop LLM prompts with high-quality prompt engineering, allowing in-house experts to design and create prompt data that guide models in generating precise outputs.

Supervised Fine-Tuning

Develop data to train and refine both existing and pre-trained models for task taxonomies. Create large scale training datasets and golden datasets for supervised fine-tuning.

Linguists, taxonomists, and subject matter experts across 85+ languages of native speakers create datasets ranging from simple to highly complex for fine-tuning across an extensive range of task categories and sub-tasks (90+ and growing).

Human Preference Optimization

Rely on human experts-in-the-loop to close the divide between model capabilities and human preferences.

Improve hallucinations and edge-cases with ongoing feedback to achieve optimal model performance through methods like RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Policy Optimization).

Model Safety, Evaluation, & Red Teaming

Ensure the reliability, performance, and compliance of your generative AI models. Assess model performance using task-specific metrics to gauge accuracy and identify potential improvements, then allowing for improved accuracy with new data.

Address vulnerabilities with Innodata’s red teaming experts. Rigorously test and optimize generative AI models to ensure safety and compliance, exposing model weaknesses and improving responses to real-world threats.
Data Collection & Creation

Data Collection & Creation

Learn More About Data Collection and Creation Services >

Naturally curate or synthetically generate a wide range of high-quality datasets across data types and demographic categories in over 85 native languages.

Our global teams rapidly collect or create realistic and diverse training datasets tailored to your unique use case requirements to enrich the training of generative AI models.

Additionally, develop LLM prompts with high-quality prompt engineering, allowing in-house experts to design and create prompt data that guide models in generating precise outputs.

0 %

of respondents in a recent survey said their organization adopted AI-generated synthetic data because of challenges with real-world data accessibility.*

  • Data Types:
    Image, video, sensor (LiDAR), audio, speech, document, and code.
  • Demographic Diversity:
    Age, gender identity, region, ethnicity, occupation, sexual orientation, religion, cultural background, 85+ languages and dialects, and more.
Supervised Fine-Tuning

Supervised Fine-Tuning

Learn More About Supervised Fine-Tuning Services >
Develop data to train and refine both existing and pre-trained models for task taxonomies. Create large scale training datasets and golden datasets for supervised fine-tuning.

Linguists, taxonomists, and subject matter experts across 85+ languages of native speakers create datasets ranging from simple to highly complex for fine-tuning across an extensive range of task categories and sub-tasks (90+ and growing).
0 %

of respondents in a recent survey said fine-tuning an LLM successfully was too complex, or they didn’t know how to do it on their own.* 

  • Sample Task Taxonomies:
    Summarization, image evaluation, image reasoning, Q&A, question understanding, entity relation classification, text-to-code, logic and semantics, question rewriting, translation…
  • SFT Techniques:
    Change-of-thought, in context learning, data augmentation, dialogue…
Human Preference Optimization

Human Preference Optimization

Learn More About Human Preference Optimization Services >

Rely on human experts-in-the-loop to close the divide between model capabilities and human preferences. Improve hallucinations and edge-cases with ongoing feedback to achieve optimal model performance through methods like RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Policy Optimization).

0 %

of respondents in a recent survey said RLHF was the technique they were most interested in using for LLM customization.* 

  • Example Feedback Types:
    DPO (Direct Policy Optimization), Simple RLHF (Reinforcement Learning from Human Feedback), Complex RLHF (Reinforcement Learning from Human Feedback), Nominal Feedback.
Model Safety, Evaluation, & Red Teaming

Model Safety, Evaluation, & Red Teaming

Learn More About Model Safety and Evaluation Services >
Ensure the reliability, performance, and compliance of your generative AI models. Assess model performance using task-specific metrics to gauge accuracy and identify potential improvements, then allowing for improved accuracy with new data.

Address vulnerabilities with Innodata’s red teaming experts. Rigorously test and optimize generative AI models to ensure safety and compliance, exposing model weaknesses and improving responses to real-world threats.
0 %

reduction in the violation rate of an LLM was seen in a recent study on adversarial prompt benchmarks after 4 rounds of red teaming.*

  • Techniques:
    Payload smuggling, prompt injection, persuasion and manipulation, conversational coercion, hypotheticals, roleplaying, one-/few-shot learning, and more…

Why Choose Innodata for Your
Generative AI Data Solutions?

Global Delivery Centers &
Language Capabilities

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

Quick Turnaround at Scale with
Quality Results

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.

Domain Expertise Across
Industries

With 4,000+ in-house SMEs covering all major domains from healthcare to finance to legal, Innodata offers expert annotation, collection, fine-tuning, and more.

Linguist & Taxonomy Specialists

Our in-house linguists and create custom taxonomies and guidelines tailored to generative AI model development.

Customized Tooling

Benefit from our proprietary tooling, including our Annotation Platform, designed to streamline team workflows and enhance efficiency in data annotation and management processes.

Why Choose Innodata for Your
Generative AI Data Solutions?

Global Delivery Centers &
Language Capabilities

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

Quick Turnaround at Scale with
Quality Results

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.

Domain Expertise Across
Industries

With 4,000+ in-house SMEs covering all major domains from healthcare to finance to legal, Innodata offers expert annotation, collection, fine-tuning, and more.

Linguist & Taxonomy Specialists

Our in-house linguists and create custom taxonomies and guidelines tailored to generative AI model development.

Customized Tooling

Benefit from our proprietary tooling, including our Annotation Platform, designed to streamline team workflows and enhance efficiency in data annotation and management processes.

Fuel Advanced AI/ML Model Development With Innodata’s Data Solutions for Generative AI.

Fuel Advanced AI/ML Model Development With Innodata’s Data Solutions for Generative AI.

Looking to Implement Generative AI Into Your Business Operations?

Innodata’s team of experts help assist in integrating generative AI models into your business operations. We will guide you through the process, from identifying strategic opportunities to implementation to ensuring continuous success.
Learn More >

Looking to Implement Generative AI Into Your Business Operations?

Innodata’s team of experts help assist in integrating generative AI models into your business operations. We will guide you through the process, from identifying strategic opportunities to implementation to ensuring continuous success.
Learn More >

Case Studies 

Generative AI Customer Success Stories

Training Text to Image Model By Providing Image Captions, Across 50+ Subject Areas

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Creating Health & Medical Dialogues Across 8+ Specialties

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Training Text to Image Model By Providing Image Captions, Across 50+ Subject Areas

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Creating Health and Medical Dialogues Across 8+ Specialties
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Training Text to Image Model By Providing Image Captions, Across 50+ Subject Areas
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Chatbot Instruction Dataset for RAG Implementation
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(NASDAQ: INOD) Innodata is a global data engineering company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data collection/annotation, AI digital transformation, and industry-specific business processes. Our low-code Innodata AI technology platform is at the core of our offerings. In every relationship, we honor our 30+ year legacy delivering the highest quality data and outstanding service to our customers.

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Training Text to Image Model by Providing Image Captions, Across 50+ Subject Areas.

  • Challenge

A leading developer of AI technology approached Innodata with a unique challenge. They were building a powerful text-to-image model capable of generating captions for advertising content across a vast range of over 50 subject areas. However, their existing solution lacked the necessary depth and accessibility for their target audience. 

  • Challenge

A leading developer of AI technology approached Innodata with a unique challenge. They were building a powerful text-to-image model capable of generating captions for advertising content across a vast range of over 50 subject areas. However, their existing solution lacked the necessary depth and accessibility for their target audience. 

  • Solution

Innodata's team of expert writers and data specialists stepped in. The team developed a comprehensive training program to enhance the AI's caption-generating capabilities, focusing on two key aspects:

  • Detailed and Accurate Descriptions: Innodata designed a multi-layered annotation process where images were deconstructed into their constituent elements. Annotators categorized objects (primary, secondary, and tertiary) and described their spatial arrangement within the image and the overall background. This ensured captions captured every significant detail with absolute accuracy .

  • Universal Accessibility: Accessibility was paramount. The team trained the AI to generate captions that adhered to clear guidelines. Metaphors and subjective language were replaced with factual descriptions, ensuring anyone, regardless of background knowledge or visual acuity, could understand the image content. Additionally, the structure of captions was designed to guide the viewer through the image in a clear and organized manner.

Detailed and Accurate Descriptions: Innodata designed a multi-layered annotation process where images were deconstructed into their constituent elements. Annotators categorized objects (primary, secondary, and tertiary) and described their spatial arrangement within the image and the overall background. This ensured captions captured every significant detail with absolute accuracy .

Universal Accessibility: Accessibility was paramount. The team trained the AI to generate captions that adhered to clear guidelines. Metaphors and subjective language were replaced with factual descriptions, ensuring anyone, regardless of background knowledge or visual acuity, could understand the image content. Additionally, the structure of captions was designed to guide the viewer through the image in a clear and organized manner.

  • Impact

The results were impressive. Innodata’s program significantly improved the AI's ability to generate comprehensive and accessible captions. Here's how it impacted our client:

  • Enhanced AI Proficiency: The AI now creates captions that provide rich detail, accurately reflecting the content of the image. This fosters trust and clarity in the user experience.

  • Accessibility at Scale: By focusing on universally understandable language, the AI can effectively cater to a broader audience, promoting inclusivity in advertising content.

  • Streamlined Workflow: The clear framework for caption structure allows for faster image comprehension, ultimately saving the client time and resources.
Enhanced AI Proficiency: The AI now creates captions that provide rich detail, accurately reflecting the content of the image. This fosters trust and clarity in the user experience.

Accessibility at Scale: By focusing on universally understandable language, the AI can effectively cater to a broader audience, promoting inclusivity in advertising content.

Streamlined Workflow: The clear framework for caption structure allows for faster image comprehension, ultimately saving the client time and resources.

Creating Health and Medical Dialogues Across 8+ Specialties

  • 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.
  • 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.

  • 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.
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.
  • 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.
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.

Chatbot Instruction Dataset for RAG Implementation:

Techniques Required Were Chain of Thought in Context Learning, and Prompt Creation Completion.

  • Challenge

A leading technology company approached Innodata with a unique challenge. They needed a specialized dataset to train their large language model (LLM) to perform complex “multi-action chaining” tasks. This involved improving the LLM’s ability to not only understand and respond to user queries but also access and retrieve relevant information beyond its initial training data.

The specific challenge stemmed from the limitations of the standard LLM, which relied solely on pre-existing patterns learned during training. This hindered its ability to perform actions requiring specific external information retrieval, hindering its functionality.

  • Challenge

A leading technology company approached Innodata with a unique challenge.

They needed a specialized dataset to train their large language model (LLM) to perform complex “multi-action chaining” tasks. This involved improving the LLM’s ability to not only understand and respond to user queries but also access and retrieve relevant information beyond its initial training data.

The specific challenge stemmed from the limitations of the standard LLM, which relied solely on pre-existing patterns learned during training. This hindered its ability to perform actions requiring specific external information retrieval, hindering its functionality.

  • Solution

Innodata implemented a creative approach to address the client's challenge:

  • Chain-of-Thought Prompt Development: Innodata’s team of experts employed a technique called “Chain of Thought in Context Learning” to design prompts that encouraged the LLM to explicitly showcase its internal thought process while responding to user queries. This provided valuable insights into the LLM’s reasoning and information retrieval steps.  

  • Prompt Completion with RAG Integration: The team leveraged “Prompt Creation Completion” techniques, where authors set up prompts, craft related queries, and complete the prompts using the Retrieval-Augmented Generation (RAG) tool. This tool retrieved relevant information necessary for the LLM to complete the task at hand.

  • Author Expertise: Our team of skilled authors, equipped with an understanding of API and RAG dependencies, crafted the dataset elements:
Chain-of-Thought Prompt Development: Innodata’s team of experts employed a technique called “Chain of Thought in Context Learning” to design prompts that encouraged the LLM to explicitly showcase its internal thought process while responding to user queries. This provided valuable insights into the LLM’s reasoning and information retrieval steps.

Prompt Completion with RAG Integration: The team leveraged “Prompt Creation Completion” techniques, where authors set up prompts, craft related queries, and complete the prompts using the Retrieval-Augmented Generation (RAG) tool. This tool retrieved relevant information necessary for the LLM to complete the task at hand.

Author Expertise: Our team of skilled authors, equipped with an understanding of API and RAG dependencies, crafted the dataset elements:
  • User-facing chatbot conversations simulating real-world interactions. 
  • Internal thought processes of the chatbot, revealing its reasoning and information retrieval steps. 
  • System-level instructions guiding the chatbot’s actions. 
  • Training on complex use cases involving multi-step tasks and subtasks. 
  • User-facing chatbot conversations simulating real-world interactions. 
  • Internal thought processes of the chatbot, revealing its reasoning and information retrieval steps. 
  • System-level instructions guiding the chatbot’s actions. 
  • Training on complex use cases involving multi-step tasks and subtasks. 
  • Impact

The resulting dataset, enriched with the "chain-of-thought" approach, offered the client significant benefits:

Enhanced LLM Functionality: The dataset equipped the LLM with the ability to perform complex, multi-action tasks, significantly improving its practical applications.

Improved Information Retrieval:  By incorporating the RAG tool, the LLM gained the ability to access and retrieve crucial information from external sources, overcoming its prior limitations.

Deeper Model Understanding: The “chain-of-thought” element provided valuable insights into the LLM’s reasoning process, enabling further optimization and development.
  • Enhanced LLM Functionality: The dataset equipped the LLM with the ability to perform complex, multi-action tasks, significantly improving its practical applications. 

  • Improved Information Retrieval:  By incorporating the RAG tool, the LLM gained the ability to access and retrieve crucial information from external sources, overcoming its prior limitations.

  • Deeper Model Understanding: The “chain-of-thought” element provided valuable insights into the LLM’s reasoning process, enabling further optimization and development.