Generative AI Data Solutions

Supervised
Fine-Tuning Data

The Foundation of Advanced LLMs

What is Supervised Fine-Tuning?

Fine-tuning involves training your AI model on curated datasets to enhance its task performance. This process teaches tasks (e.g., classification), scenarios (e.g., following instruction dialogs), and skills (e.g., reasoning).

Innodata combines expert-created datasets with cutting-edge methodologies to help your model excel in real-world applications.

Comprehensive Multimodal
Fine-Tuning Capabilities.

Innodata can tackle simple to highly complex fine-tuning scenarios across an expanding list of categories of tasks and subtasks across multiple domains, languages, and modalities.

Fine-Tuning
Tasks + Subtasks.

Content Creation

Conversation Analysis

Document Info

Editing

Logic + Semantics

Question Answering

Answer Verification
Answerability Classification
Explanation:
(How it works, idiom meaning)
Suggestion:
(E.g., breakfast suggestion)
Fact Verification
Question Decomposition
Question Generation
Question Rewriting
Question Understanding
Recommendation
Multiple choice QA
Input inversion (Jeopardy style)
Closed QA / Open QA

Textual Information

Programming

Translation

Source Language to Target Language

Image + Text (Vision-Language Tasks)

Image Captioning
Image Generation from Text
Image Retrieval from Text Queries
Text-to-Image Alignment
Visual Question Answering (VQA)
Image Classification with Text Descriptions
Object Detection with Descriptive Text
Scene Understanding from Descriptions
Image-Text Matching
Cross-Modal Retrieval (Image to Text, Text to Image)

Audio + Text (Speech-Language Tasks)

Speech Recognition
Speech Synthesis (Text to Speech)
Speech-to-Text Translation
Audio Captioning
Audio Sentiment Analysis
Speaker Identification from Audio
Speech Emotion Detection
Sound Event Detection and Classification
Audio Retrieval from Text Queries
Spoken Dialogue System Fine-Tuning

Image + Audio Tasks

Audio-Visual Event Detection
Sound Source Localization in Video
Action-Sound Correlation
Audio-Visual Scene Understanding
Audio-Visual Synchronization in Videos

Video + Text (Video-Language Tasks)

Video Captioning
Video Generation from Text
Video Summarization
Action Recognition in Video
Video Question Answering (VQA for Video)
Video Retrieval from Text Queries
Video-Text Alignment
Event Detection in Videos with Text Descriptions
Video Segmentation with Text Instructions

Multimodal Reasoning + Fusion Tasks

Multimodal Sentiment Analysis
Audio-Visual Speech Recognition (lip reading)
Multimodal Dialogue Generation
Multimodal Question Answering (text, image,
and audio)
Audio-Visual Synchronization
Multimodal Named Entity Recognition
Multimodal Emotion Detection

Sensor Data + Text or Audio/Video

Sensor Data Interpretation with Text
Multimodal Sensor Fusion
Gesture Recognition (Sensor + Video)

Multimodal Knowledge + Memory Tasks

Multimodal Knowledge Graph Creation
Cross-Modal Retrieval from Multimodal Databases

General Multimodal Tasks

Multimodal Coherence Classification
Multimodal Entailment

Scenarios.

Chain-of-Thought + In-Context Learning

Series of reasoning steps laying out variables and building up final answer.

Data Augmentation

Imitation data review, input inversion and
contrast/perturbations.

Dialog

Turn-by-turn conversations.

Full Length

Original content, professional summaries, complex documentation, systematic reviews.

How Innodata Accelerates Your Generative AI Fine-Tuning.

Mastering Complexity

We excel in creating training datasets for even the most complex fine-tuning tasks. Our expertise spans diverse modalities, a multitude of languages, and nuanced domain-specific content.

Domain-Specific Expertise

Innodata accelerates your generative AI initia tives with a global network of 5,000+ in-house SMEs across all major domains. Our SMEs hold advanced degrees, including Masters and PhDs, and possess deep industry knowledge for any dataset need.

High-Precision Training Data

Our expert teams craft high-quality training datasets that cater to a vast array of supervised fine-tuning scenarios. This data encompasses diverse modalities (text, image, video, audio, code) and over 85 languages and dialects.

Enabling Domain-Specific
Fine-Tuning Across Industries.

Agritech + Agriculture

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

Energy, Oil, + Gas

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

Media + Social Media

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

Consumer Products + Retail

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

Manufacturing, Transportation, + Logistics

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

Banking, Financials, + Fintech

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

Legal + Law

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

Automotive + Autonomous Vehicles

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

Aviation, Aerospace, + Defense

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

Healthcare + Pharmaceuticals

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

Insurance + Insurtech

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

Software + Technology

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

Let’s Innovate Together.

See why seven of the world’s largest tech companies trust Innodata for their AI needs.

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We could not have developed the scale of our classifiers without Innodata. I’m unaware of any other partner than Innodata that could have delivered with the speed, volume, accuracy, and flexibility we needed.

Magnificent Seven Program Manager,
Al Research Team

CASE STUDIES

Success Stories

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

Question + Answering for Global Tech Company

Intelligent Regulatory Insights with Machine Learning and OpenAI

Generative AI Solutions for a Leading Information Publisher

Image Caption Generation

Streamlining Regulatory Content Management with Automation and Retrieval-Augmented Generation (RAG)

Text Generation in the Advertising Space

Base Annotations Comparison

Enhancing Summarization Accuracy for Compliance

Search Summarization

Chatbot Instruction Dataset for RAG Implementation

Creating Health and Medical Dialogues Across 8+ Specialties

Events, News & Events

Innodata Unveils Generative AI Test and Evaluation Platform, Built with NVIDIA Technology

Blog

AI Red Teaming in Enterprises: Strengthen Your Model Security

Events, News & Events

Innodata to Showcase GenAI Test and Evaluation Platform at NVIDIA GTC 2025

Blog

How to Ensure Your Large Language Model (LLM) is Accurate and Safe

What is fine-tuning, and why is it important?

Fine-tuning is the process of refining a pre-trained AI model using domain-specific data to improve performance for a particular task. AI fine-tuning allows organizations to customize AI outputs, enhancing accuracy and relevance.

How does fine-tuning AI models improve performance?

Fine-tuning AI models tailors them to specific use cases by training on curated datasets. This process ensures that AI systems generate more precise and context-aware responses, improving efficiency across industries.

What are the benefits of fine-tuning large language models?

Fine-tuning large language models enhances their ability to understand specialized language, domain-specific terminology, and nuanced user intents. This is essential for businesses that require AI to align with industry-specific knowledge.

How does fine-tuning foundation models differ from training a model from scratch?

Fine-tuning foundation models leverages pre-trained AI architectures, requiring significantly less data and computational power compared to training a model from the ground up. This process refines existing capabilities rather than developing new ones.

What role do fine-tuning datasets play in the optimization process?

Fine-tuning datasets are carefully curated sets of data used to train AI models for specific applications. High-quality datasets ensure that generative AI fine-tuning improves accuracy, reduces biases, and aligns AI outputs with business needs.

What industries benefit most from fine-tuning AI?

Industries such as healthcare, finance, legal, and retail benefit from fine-tuning AI models to understand industry-specific terminology, regulatory language, and customer interactions more effectively.

How does fine-tuning generative AI enhance model outputs?

Generative AI fine-tuning refines an AI model’s ability to create high-quality text, images, or other content. By optimizing fine-tuning datasets, businesses can ensure that AI-generated outputs are aligned with brand voice, compliance requirements, and user expectations.

How often should businesses consider fine-tuning AI models?

Businesses should periodically engage in fine-tuning AI models to keep up with evolving data trends, regulatory updates, and user preferences. Regular updates ensure AI continues to perform optimally and remains relevant to business needs.

What challenges come with fine-tuning large language models?

Challenges in fine-tuning large language models include the need for high-quality fine-tuning datasets, computational resources, and expertise in selecting the right training techniques. However, when done correctly, it significantly enhances AI model capabilities.