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Generative AI

Fueling Generative AI Model Development
and Implementation

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Innodata is more than
high-quality data engineering,
we’re your trusted partner in AI advancement.

Innovate faster, solve complex model problems, and stay ahead of the curb with Innodata’s Generative AI solutions

Services Supporting Your
Model's Full Lifecycle

Fine-Tuning

Go beyond generic language processing by customizing large language models to suit your domain and tasks. This targeted training refines the model's understanding of your unique terminology, data patterns, and desired outputs
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Data Collection &
Creation

Generative AI models require diverse data for learning. Our data collection, combining natural and synthetic data, prepares your AI for the world. This comprehensive approach provides precise training sets for tailored solutions to your specific use cases.
Discover Data Collection & Creation Services >

Reinforcement Learning from Human Feedback

Bridge the gap between model capabilities and human preference, continuously refine large language models through a feedback loop. Humans guide the model's evolution, shaping its outputs to be factually accurate, bias-free, and stylistically tailored.
Discover RLHF Services

Model Evaluation
& Testing

Test and maintain LLMs to ensure continued safety and compliance. Our experts act as ethical adversaries, uncovering vulnerabilities with techniques like red teaming, giving you a complete picture of your AI's potential blind spots. Discover Model Evaluation & Testing Services >

Fine-Tuning

Go beyond generic language processing by customizing large language models to suit your domain and tasks. This targeted training refines the model's understanding of your unique terminology, data patterns, and desired outputs
Discover Fine-Tuning Services >

Data Collection & Creation

Generative AI models require diverse data for learning. Our data collection, combining natural and synthetic data, prepares your AI for the world. This comprehensive approach provides precise training sets for tailored solutions to your specific use cases.
Discover Data Collection & Creation Services >

Reinforcement Learning from Human Feedback

Bridge the gap between model capabilities and human preference, continuously refine large language models through a feedback loop. Humans guide the model's evolution, shaping its outputs to be factually accurate, bias-free, and stylistically tailored.
Discover RLHF Services

Model Evaluation
& Testing

Test and maintain LLMs to ensure continued safety and compliance. Our experts act as ethical adversaries, uncovering vulnerabilities with techniques like red teaming, giving you a complete picture of your AI's potential blind spots.
Discover Model Evaluation & Testing Services >

Harnessing the power of Generative AI for your business demands strategic planning and expert support. We offer a structured, collaborative journey that empowers you to
seamlessly integrate AI-powered solutions, maximizing your ROI and achieving tangible results.

Talk to an Expert

Your Generative AI Journey: From Ideation to Impact

Looking to Implement Generative AI in Your Business?

Ready to explore the
possibilities?

Talk to an Expert

Prepare

Lay the foundation with model selection, diverse data sourcing, and tailored instruction datasets. 

Orchestrate

Stay ahead of the curve with ongoing optimization, model drift management, and performance monitoring. 

Ideate

Ignite innovation through collaborative workshops and expert business case development.

Implement

Unleash the power with custom-built AI solutions, fine-tuning, prompt engineering, and rigorous model testing and evaluation. 

Keeping Your Models Ahead of
the Competition

  • Boost AI Model Performance
    Our high-quality, domain-specific data, collected and processed by our global network of subject matter experts (SMEs), fuels models that out-perform the competition.
  • Get Faster Time to Market
    Our approach and customized solutions ensure we adapt to your unique needs and deliver results quickly, accelerating your AI journey.
  • Drive Business Impact
    We don’t just deliver data, we deliver results. Our proven track record of building world-class AI models for leading companies speaks for itself.
  • Leverage Automation
    Streamline data collection, processing, and training, improving efficiency and reducing costs.
  • Develop Responsible AI
    Upholding privacy, fairness, transparency and ethics.
  • Boost AI Model Performance
    Our high-quality, domain-specific data, collected and processed by our global network of subject matter experts (SMEs), fuels models that out-perform the competition.
  • Get Faster Time to Market
    Our approach and customized solutions ensure we adapt to your unique needs and deliver results quickly, accelerating your AI journey.
  • Drive Business Impact
    We don’t just deliver data, we deliver results. Our proven track record of building world-class AI models for leading companies speaks for itself.
  • Leverage Automation
    Streamline data collection, processing, and training, improving efficiency and reducing costs.
  • Develop Responsible AI
    Upholding privacy, fairness, transparency and ethics.

Ben Cook Founder & ML Engineer, Sparrow Computing

Innodata really adds value to our operations. We trust theInnodata team to help us transform the data into training-ready annotations, which allows us to move quicklythrough the algorithm development lifecycle. Thanks toInnodata, we were able to quickly correct/edit some model-derived predictions, get the model re-trained, and pusha new version of an inspection model that performed […]

Ways Our Customers Have

Applied Generative AI

Innodata enhances your AI initiatives with applications across various data types including text, image, video, sensor, audio, database and code-driven data: 

  • Code Review
  • Conversational AI
  • Avatar Generation
  • Content Toxicity Identification
  • Sentiment Analysis
  • Fact Checking
  • Image/Video Generation
  • Document Summarization
  • Text to Image, Video, Audio
  • And more...

Gain the Edge with Innodata

In-House Data Scientists

Leverage the combined expertise of our data scientists and engineers working seamlessly alongside your team to accelerate your AI initiatives. 

Global Delivery Centers and Language Capabilities
Innodata boasts an extensive global delivery network that encompasses native speakers of 40+ languages in over 20 strategic locations. We have the infrastructure to seamlessly incorporate new languages across any time zone, and the ability to establish additional global delivery centers tailored to your needs.
Domain Expertise

With 4,000+ in-house SMEs spanning all industries. By partnering with Innodata, organizations can ensure generative AI models are trained on leading domain-specific multilingual data. 

Authoring

Innodata offers content authoring, technical writing and rewriting, editing for grammar and style, and summarization for precise LLM development. 

Implementation Expertise

If you’re implementing generative AI, we offer a structured, collaborative process that guides you from understanding the possibilities to deploying a customized solution tailored to your unique business needs. Our vision workshop brings together expert guidance, data-driven insights, and a clear roadmap, ensuring you maximize ROI and achieve tangible results.  

Transparent and Secure Solutions

Innodata champions responsible AI development through data certification, ensuring your models are built on high-quality, ethically sourced datasets. We prioritize data transparency, providing clear lineage and auditability throughout the development process. This commitment to data integrity fosters trust and empowers you to confidently deploy your AI solutions. 

One partner, endless possibilities

Everything you need for Generative AI, all under one roof. Streamline your journey, maximize your results. 

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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Training Text to Image Model By Providing Image Captions, Across 50+ Subject Areas (1)

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 (1)

Creating Health & Medical Dialogues Across 8+ Specialties

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Chatbot Instruction Dataset for RAG Implementation (1)

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

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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.

Success Stories

Chatbot Instruction Dataset for RAG Implementation

  • Innodata Case Study Challenge Icon 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.

  • Innodata Case Study Solution Icon 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:

  • 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. 
  • Innodata Case Study Impact Icon 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.