Data Solutions
Generative AI
Generative AI: Making business processes smarter, not harder.
Bring intelligence to your enterprise processes. Whether you have existing generative AI models or want to integrate them into your operations, we offer a comprehensive suite of services to unlock their full potential.
Code Review
Our expert engineers conduct quality checks on code output to ensure it is well-structured, efficient, and error-free. We actively suggest improvements and generate alternative implementations for optimized outcomes. This step is crucial in enhancing the AI model’s performance.
Image/Video Models
Generative AI models can create high-quality images, videos, and avatars. These models use advanced algorithms to generate realistic and customizable content that is revolutionizing the way we use and consume visual media. Enriching and quality checking outputs is critical for precise training.
Captioning and Metadata
Our skilled and experienced annotators use precise and accurate visual captioning techniques including metadata descriptions to train models for more realistic and comprehensive results.
NSFW Content
NSFW content moderation and data annotation services to help companies dealing with user-driven AI-generated content. This ensures a safe user experience by identifying and removing inappropriate or offensive content, and by labeling and categorizing data for AI models to recognize patterns and accurately predict outcomes.
Caption Classification
Caption Ranking
Human-generated image, video, and avatar caption ranking and ordering. This process sorts captions from best to worst, identifies the most accurate and relevant annotations or provides new options to improve the quality of your model’s training data.
Watermarks
Watermark identification service removes watermarks from images, videos, and avatar data used for AI model training to prevent interference and ensure accuracy. Our advanced algorithms and human expertise are utilized to locate and detect watermarks, and the results are fed back into model training data.
Blurry/Illegible Image and Character Classification
Innodata offers services for classifying blurry/illegible images, which are often a problem in training generative AI models. Our service uses human annotators and advanced image processing to classify images and text based on quality. This can also be useful for OCR use cases analyzing unstructured data within images, ensuring accurate and reliable training data for AI models.
Large Language Models
Summarization
NSFW Content
Quality Assurance and Output Grading/Evaluation
Hallucinations
Ethical Data Collection
Our processes adhere to rules of privacy, autonomy, and dignity, ensuring accurate and unbiased data, and protecting data privacy and security. We are the experts in sourcing and generating speech, audio, image, video, text, and document data to meet any industry domain need, helping to develop and deploy responsible AI and generative systems in an ethical manner.
Workshops & Fine Tuning
Visioning Workshops
A tried and tested methodology that helps companies understand the value and capabilities of implementing generative AI into their own organizations. Our team will assess your organization’s current state and identify the areas where ChatGPT, Bard, Claude or any generative AI model could improve business outcomes. Innodata can also help to bring together key stakeholders from different departments within the organization to build consensus around the goals and objectives of implementing generative AI.
Domain Adaptation
Innodata can assist in adapting your existing generative AI models and processes to perform well in a new domain, or in a specific context or application, by reusing or fine-tuning the model’s parameters. Customize and fine-tune your models, prepare and create new or augmented high-quality training data, and provide continuous improvement and optimization in your generative AI initiatives.
Process Management
Our experts analyze your company’s existing processes, identify areas where generative AI could be used to improve efficiency, reduce costs, and enhance outcomes. Our process-first approach involves a thorough assessment of the potential benefits of incorporating generative AI, like models from OpenAI, Google, Midjourney, and more. We then collaborate with your team to plan the design and implementation solution that integrates with your existing processes.
Model Deployment
Once a generative AI model has been developed, it needs to be deployed to production in order to start generating results. This process can be complex and requires careful planning and execution to ensure that the model performs accurately and reliably in a production environment. Infrastructure setup, deployment strategy, testing and validation, monitoring, and maintenance help ensure that your generative AI models are deployed effectively, successfully, and perform reliably over time.
Red Teaming
As generative AI is increasingly used in real-world applications, the risk of vulnerabilities, misuse, compromised data, and model security rises. Our teams of specialists conduct red-teaming, jailbreaking, and ethical hacking tests to help mitigate risks and identifying vulnerabilities. Rely on the experts to identify a model’s susceptibilities to attacks and ensure that appropriate measures are in place to safeguard against potential threats.
Authoring & Domain
With 4,000+ in-house SMEs spanning all industries, Innodata offers subject matter expert content authoring, technical writing and rewriting, PII redactions, grammatical and syntactical editing, and summarization in over 40+ languages. By leveraging the expertise and capabilities of Innodata’s SMEs, organizations can ensure their generative AI models are trained on leading domain-specific data, enabling them to accurately generate first-class outputs across multiple languages and industries.
How We Are Helping Our Customers
Implement Generative AI
Rewriting Responses
A golden set of conversational AI responses was created by our Innodata team for a global technology company to train their AI assistant. The team reviewed and rewrote chatbot responses to meet the company’s guidelines for creativity, style, grammar, and syntax.
Evaluating Code
Reviewing programming codes generated by an AI chatbot to train an AI model to respond correctly to user prompts: Innodata’s team of experienced programmers employed a multi-pass approach to ensure consistency and reduce subjectivity in the rating process. They also updated guidelines to clarify requirements and provided additional examples to measure various project and individual metrics such as reviewer agreement rates and match rates.
Rating Responses
A global tech firm’s chatbot responses were assessed for accuracy, helpfulness, and appropriateness using a double-blind process with third-party arbitration.
Assessing Model Outputs
Evaluating the quality of LLM outputs in response to user prompts: Innodata used a large team of US-based raters to rate the helpfulness of the model output on a given input-output pair while also checking for constraints, irrelevant information, language quality, and correctness.
Improving Model Performance
Improving the AI models used in a cloud-scale business intelligence service: Innodata set up teams with specific expertise and skillsets in multiple locations for various annotation tasks such as detecting question ambiguity, writing questions, answering questions, reviewing multi-view relevance, and selecting insights.
