Data Solutions for AI/ML
Data Solutions for Traditional and Generative AI Model Development
Train and Deploy
Traditional and Generative AI Models with Confidence.
Innodata offers industry-leading data solutions to help you build, train, and deploy powerful Traditional and Generative AI models. With a global workforce, Innodata provides high-quality data for:
Prompt Engineering
RAG Development
Supervised Fine-Tuning
Red Teaming
Data Annotation
Data Collection/Creation
Fine-Tuning
Human Preference Optimization
Model Safety
Data Annotation
Data Annotation
Fuel your traditional and generative AI/ML models with high-quality annotated training data. Our team of subject matter experts delivers accurate, reliable, and domain-specific data annotation services across all data types in 85+ languages.
Image, Video, & Sensor Annotation: From faces to places, power your visual-based and computer vision models with high-quality annotated data.
Text Annotation: Train your models with high-quality data annotated from the most complex text, code, and document sources.
Speech & Audio Annotation: Scale your audio-based AI/ML models and ensure model flexibility with diverse speech data in 40+ languages.
0
%
of a data scientist’s time is built building training datasets, according to a leading cloud computing enterprise.*
-
Data Types:Image, video, sensor (LiDAR), audio, speech, document, and code.
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Expertise Across Industries:Healthcare, finance, insurance, law, agritech, retail, autonomous vehicles, logistics, manufacturing, aviation, defense, and more…
Data Collection/Creation
Data Collection & Creation
Let Innodata source and collect speech, audio, image, video, text, and document training data for generative and traditional Al model development. We support 85+ languages worldwide and offer customized data collection services to meet any domain requirements.
Whether you need natural data collection, studio data capture, or on-the-ground data gathering, Innodata delivers custom datasets tailored to your unique model training needs.
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.
Fine-Tuning
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).
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
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
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.
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…
Data Annotation
Data Collection/Creation
Fine-Tuning
Human Preference Optimization
Model Safety
Data Annotation
Data Annotation
Fuel your traditional and generative AI/ML models with high-quality annotated training data. Our team of subject matter experts delivers accurate, reliable, and domain-specific data annotation services across all data types in 85+ languages.
Image, Video, & Sensor Annotation: From faces to places, power your visual-based and computer vision models with high-quality annotated data.
Text Annotation: Train your models with high-quality data annotated from the most complex text, code, and document sources.
Speech & Audio Annotation: Scale your audio-based AI/ML models and ensure model flexibility with diverse speech data in 40+ languages.
0
%
of a data scientist’s time is built building training datasets, according to a leading cloud computing enterprise.*
-
Data Types:Image, video, sensor (LiDAR), audio, speech, document, and code.
-
Expertise Across Industries:Healthcare, finance, insurance, law, agritech, retail, autonomous vehicles, logistics, manufacturing, aviation, defense, and more…
Data Collection/Creation
Data Collection & Creation
Let Innodata source and collect speech, audio, image, video, text, and document training data for generative and traditional Al model development. We support 85+ languages worldwide and offer customized data collection services to meet any domain requirements.
Whether you need natural data collection, studio data capture, or on-the-ground data gathering, Innodata delivers custom datasets tailored to your unique model training needs.
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.
Fine-Tuning
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).
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
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
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.
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 AI/ML Initiatives?
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 5,000+ 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 Traditional and Generative AI with Innodata.
