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Ideate > Pilot > Implement > Maintain Implement Generative AI Into Your Enterprise

Ideate > Pilot > Implement > Maintain
Implement Generative AI Into Your Enterprise

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Innodata offers expertise in implementing generative AI models into enterprise business operations. From ideation to maintenance, we guide your journey through innovative vision workshops, exploratory pilot execution, seamless implementation, and ongoing model maintenance.

Innodata offers expertise in implementing generative AI models into enterprise business operations. From ideation to maintenance, we guide your journey through innovative vision workshops, exploratory pilot execution, seamless implementation, and ongoing model maintenance.

1 Ideate

Align with your business objectives, define automation roadmaps, and find where generative AI can foster innovation within your organization with Innodata’s consultative vision workshops. We bring design thinking and strategic planning to establish the right balance of person/machine synergy.
Schedule a Vision Workshop

1

Ideate

Align with your business objectives, define automation roadmaps, and find where generative AI can foster innovation within your organization with Innodata’s consultative vision workshops. We bring design thinking and strategic planning to establish the right balance of person/machine synergy.

Schedule a Vision Workshop

We Offer

Facilitating collaborative sessions to understand your business goals and challenges and identify potential areas for generative AI applications.

Employing human-centered design principles to develop creative solutions tailored to your specific needs.

Creating a strategic roadmap outlining the steps involved in implementing your chosen generative AI solution.

Working with you to define problems, potential solutions, and expected ROI.

Analyzing your workflows to ensure integration with generative AI is feasible and beneficial.

  • Vision Workshops: Facilitating collaborative sessions to
    understand your business goals and challenges and identify
    potential areas for generative AI applications.

  • Design Thinking: Employing human-centered design principles
    to develop creative solutions tailored to your specific needs.
  • Roadmap Defining: Creating a strategic roadmap outlining the
    steps involved in implementing your chosen generative AI solution.
  • Business Case Definition: Working with you to define problems,
    potential solutions, and expected ROI.
  • Process Validation: Analyzing your workflows to ensure
    integration with generative AI is feasible and beneficial.

2 Pilot

The Pilot phase focuses on MVP (minimum viable product) realization through model configuration and data preparation for your specific business case(s). We assess and cleanse your existing data or augment it through natural data collection or synthetic generation per your specifications. If applicable, our team will also provide human preference optimization services or create data for supervised fine-tuning / fine-tune. This stage sets the foundation for full-scale implementation post-pilot.
Schedule a Vision Workshop

2

Pilot

The Pilot phase focuses on MVP (minimum viable product) realization through model configuration and data preparation for your specific business case(s). We assess and cleanse your existing data or augment it through natural data collection or synthetic generation per your specifications. If applicable, our team will also provide human preference optimization services or create data for supervised fine-tuning / fine-tune. This stage sets the foundation for full-scale implementation post-pilot.

Schedule a Vision Workshop

We Offer

Our expert team will assess key model questions, such as choosing between OpenAI API or Azure Cloud, determining parameter size (e.g., 7 billion vs. 70 billion), deciding between cloud or private hosting, etc.

Our team collects your data, then carefully cleanses, and normalizes it, ensuring high-quality and readiness for model training.

According to your specifications, our teams can naturally curate or synthetically generate data over a wide range of data types and 85+ languages to address training
data limitations.

By utilizing HPO (human preference optimization) or by creating data for supervised fine-tuning, we support tuning MVP models to adapt to your specific use case(s).
  • MVP Model Configuration: Our expert team will assess key model questions, such as choosing between OpenAI API or Azure Cloud, determining parameter size (e.g., 7 billion vs. 70 billion), deciding between cloud or private hosting, etc.

  • Data Management: Our team collects your data, then carefully cleanses, and normalizes it, ensuring high-quality and readiness for model training.

  • Data Augmentation: According to your specifications, our teams can naturally curate or synthetically generate data over a wide range of data types and 85+ languages to address training data limitations.

  • HPO and Data for Fine-Tuning: By utilizing HPO (human preference optimization) or by creating data for supervised fine-tuning, we support tuning MVP models to adapt to your specific use case(s).

3

3 Implement

The Pilot phase focuses on MVP (minimum viable product) realization through model configuration and data preparation for your specific business case(s). We assess and cleanse your existing data or augment it through natural data collection or synthetic generation per your specifications. If applicable, our team will also provide human preference optimization services or create data for supervised fine-tuning / fine-tune. This stage sets the foundation for full-scale implementation post-pilot.

Schedule a Vision Workshop
Implement

Transitioning your models from pilot to production. First, your designated implementation team will focus on integrating models into your workflows. Next, we will assist in guiding the MVP (minimum viable product) model to meet the accepted criteria for production success — utilizing techniques like natural or synthetic data collection, human preference optimization, and data creation for supervised fine-tuning. If applicable, the team will then address model evaluation, safety, and perform red teaming to address model weaknesses. And finally, user acceptance testing is conducted to ensure a successful transition to production.

Schedule a Vision Workshop

We Offer

 Our team seamlessly integrates your model into your existing production flows, ensuring smooth operations and efficient data flow.

According to your specifications, our teams can enrich your production training data with naturally curated or synthetically generated data over a wide range of data types and 85+ languages.

By utilizing HPO (human preference optimization) or by creating data for supervised fine-tuning, we support tuning production models for increased performance.

 Red teaming experts assess model safety and weaknesses using task-specific metrics to gauge accuracy and identify potential improvements, then allowing for improved accuracy with new data.

Thorough user acceptance testing to ensure the model meets your specific requirements and delivers the expected business value.

  • Model Integration: Our team seamlessly integrates your model into your existing production flows, ensuring smooth operations and efficient data flow.

  • Data Augmentation: According to your specification, our teams can enrich your production training data with naturally curated or synthetically generated data over a wide range of data types and 85+ languages.

  • HPO and Data for Fine-Tuning: By utilizing HPO (human preference optimization) or by creating data for supervised fine-tuning, we support tuning production models for increased performance.

  • Model Safety, Evaluation, and Red Teaming: Red teaming experts assess model safety and weaknesses using task-specific metrics to gauge accuracy and identify potential improvements, then allowing for improved accuracy with new data.

  • Production User Acceptance Testing: Thorough user acceptance testing to ensure the model meets your specific requirements and delivers the expected business value.

4

4 Maintain

The Pilot phase focuses on MVP (minimum viable product) realization through model configuration and data preparation for your specific business case(s). We assess and cleanse your existing data or augment it through natural data collection or synthetic generation per your specifications. If applicable, our team will also provide human preference optimization services or create data for supervised fine-tuning / fine-tune. This stage sets the foundation for full-scale implementation post-pilot.
Schedule a Vision Workshop
Maintain

Ensuring your models stay ahead of the curve, adapting to changing data patterns and user behaviors. Using a holistic approach, we provide ongoing maintenance into the tangible impacts of your AI initiatives. Our expertise covers critical aspects like managing model drift, model hosting, and efficient performance monitoring.

Schedule a Vision Workshop

We Offer

Continuously monitoring the potential for performance degradation as data and user behavior evolve.

Regularly evaluating the model’s
performance and identifying areas for further optimization, tracking realized vs. planned benefits.

Implement robust error-handling mechanisms to ensure continued operation and address potential issues proactively.

Ensure your model is securely deployed and accessible within your infrastructure.

Streamline deployment processes for efficiency and scalability.

  • Model Drift Management: Continuously monitoring the potential for performance degradation as data and user behavior evolve.

  • Performance Monitoring: Regularly evaluating the model’s performance and identifying areas for further optimization, tracking realized vs. planned benefits.

  • Error Handling: Implement robust error handling mechanisms to ensure continued operation and address potential issues proactively.

  • Model Hosting: Ensure your model is securely deployed and accessible within your infrastructure.

  • Pipeline Automation: Streamline deployment processes for efficiency and scalability.

Empowering Businesses Across Use Cases

Innodata empowers businesses to implement generative AI models across a wide range of use cases, including:

Call Centers

Call Summarization
Customer Q&A
Sentiment Analysis
Call Analytics
Follow-Up Email Generation
Content Generation

Risk & Compliance

  • Legal Document Summarization
  • Case & Litigation Documents Search
  • Case Documentation Relationship Analysis
  • Compliance Document Search and Relationship Analysis
  • Legal Assistants
  • Medical Transcription and Validation

Marketing

  • Content Generation
  • Social Media Summarization
  • Social Media Sentiment Analysis & Trends
  • Campaign Content Generation
  • Product Content Generation (for Sale, Documentation, Search)

HR

Sentiment Analysis
Intelligent Onboarding
Virtual Assistants
Q&A in Internal Knowledge
Management Chatbots

And More…

Deploy Leading Generative
AI with Innodata’s
Implementation Services

Deploy Leading Generative AI with Innodata’s Implementation Services

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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(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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  • 201-371-8000
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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.