Data Labeling Services for Generative AI & LLMs

We add a human touch to curation and preparation of your datasets as we understand that creating a generative AI model that produces fresh content depends on accurately labeled and annotated training datasets.

Human input for generative AI involves merging the power of AI with human intellect, hence creating a balance between technology and human oversight.

Contact Us Now
Data Labeling for Generative AI and LLM

Generative AI Precision: Discover Our Service Spectrum

Fine Tuning

Refine Input Effectiveness

Discover more


Improve Output Accuracy

Discover more


Ethically Sourced Data

Discover more

Preventing AI Cannibalism via 100% Original Content

As it’s a well-known fact, the Internet is increasingly getting flooded with low quality content which is creating obstacles in training of new AI models resulting in AI cannibalism. We make sure that our data is 100% authentic by ensuring that our workforce researches the Internet and other sources for producing content without relying on any foundation models or AI tools.

ai foundation models

Data Labeling for Foundation Models

Foundation LLMs require vast amounts of data for training which must be labeled correctly to make accurate predictions. This ensures that the data remains balanced and represents real-world use cases. Human input for LLM is necessary for ensuring safety of your generative AI in language model and detecting any bias in the output.

A mix of natural language processing (NLP) and human moderation can be used for detecting any offensive content in LLM output. We pride in our capability to produce content that’s 100% original.

Stages in Large Language Model Development

We have over a decade of experience in creating datasets for LLM. We can assist you in building a data pipeline to cater to your needs.

  • 1. Pre-Training
  • Pre-Training
  • • Internet/Client
Stage 1
  • 2. Fine-Tuning
  • Fine-Tuning
  • • Creation of Prompts
  • • Around 100k Data Points
Stage 2
  • 3. RLHF
  • RLHF
  • • Verifying Output

Human Annotators

Human Annotator in Generative AI
  •  Data collection  and cleaning
  • • Data Collection & Cleaning
  • Producing and categorizing prompts
  • • Producing & Categorizing Prompts.
  • Evaluating answers and creating prompts
  • • Evaluating Answers & Creating Prompts.


A large amount of data is gathered from Internet or other sources. Data is collated and cleansed by our expert human annotators. This is a time-consuming and expensive process owing to the size of the dataset and the complexity of parameters.

Pre-training assists data scientists in obtaining the right mix of data which fulfils business goals, reduces biases, and hallucination risk. A cleansed data invariably enhances the performance of your LLM.

We offer labeling services for processing generative AI image, video, audio, text, and tabular datasets.

Pre-Training in Generative AI

Our Generative AI Labeling Services

Image Datasets

To train and generate new visual content, generative AI relies on image datasets which are classified, detected, and segmented using image datasets that consist of large collections of labeled or unlabeled images. Generative AI models are hence developed using these datasets.

Text Datasets

Generative AI webpage text datasets are an essential component of natural language processing (NLP) models. These datasets are carefully curated collections of text data that are used to train artificial intelligence models to generate coherent and meaningful language.

Audio/Video Datasets

Audio and video datasets are used to train generative AI models. These datasets are used to generate audio content such as music and audio synthesis. Datasets include collections of audio recordings, including single sounds and full-length songs used to train machine learning models.

Tabular Datasets

From financial analysis to predictive modeling, tabular datasets are frequently used to train generative models. In tabular data, data imputation is a common application of generative models.


To create your own LLM, labeled high quality data needs to be fine-tuned. For this, human feedback is required which is provided by us. Fine-tuning involves tagging queries with prompts. Almost hundred thousand data points are created. Fine-tuning involves creation of better summaries or answering questions to have a better dialog.

Prompt Engineering Services

To this end, we offer prompt engineering solutions that involve designing, testing, deployment, and delivery of prompts for a wide range of generative AI applications.

Prompt Engineering Services

Reinforcement Learning from Human Feedback (RLHF)

Large language models display tremendous potential, however they need to be evaluated to ensure their performance is up to mark. We deploy RLHF for evaluating large language models with the aim of verifying output, evaluating output, and creating relevant prompts/instructions.

RLHF Services

We offer RLHF which is a specialized service that improves the delivery or the output accuracy of AI and machine learning models.

RLHF Services

Our Capabilities

We utilize our AI training data expertise and the uninterrupted workflow to have the data up and running quickly.

Setting Pipeline

Setting Pipeline

We help you in setting up a well-functioning moderation pipeline to ensure your LLM output complies with your corporate policies.

LLM Annotators

LLM Annotators

We have LLM data annotators with excellent English reading and writing capabilities to answer prompts or questions.

LLM quality reviewers

LLM Quality Reviewers

We have LLM quality reviewers for evaluating model responses to prompts, and quality checking annotators’ prompt responses.

Domain-Specific LLM

Domain-Specific LLM

We have SME within our team for developing domain-specific datasets for LLM. We can also hire SME from various domains to build domain-specific LLM.



We have expertise in STEM (Science, Technology, Engineering, and Math) for developing datasets for your LLMs.



We support a wide range of languages for moderating user-generated content spoken across the globe.



We ensure your data is 100% accurate.



We ensure data security as we value our customers at every step of the way.

Large Language Model Domains

Accounting Agriculture Architecture Astronomy Aviation Biology Business Management Chemistry Computer Science Conservation and Ecology Economics Education Electrical Engineering Engineering Finance Geography History Journalism Law Liberal Arts
Use Cases

Generative AI can improve healthcare to a large extent by lowering down costs, improving operational efficiency, drug discovery, diagnosing diseases, and a lot more.


Generative AI can revolutionize the fintech industry by offering personalized customer solutions customized to meet every customer’s needs and cost-efficient operations.

Digital Marketing
Digital Marketing

Generative AI can benefit digital marketing in content generation by writing blog posts, social media updates, product descriptions, etc. It can tailor content to meet individual needs.


Generative AI chatbots are trained from huge datasets to assist them in understanding natural language in a better way than previous agents. Moreover, it can assist in generating creative content like poems, songs, short stories, essays, etc.

Media and Entertainment
Media and Entertainment

Generative AI hastens research by synthesizing and analyzing vast amount of information and creating summaries of editorial content. This can hasten the post-production process.

Autonomous vehicles
Autonomous vehicles

They utilize intelligent algorithms to enable the vehicle to comprehend and interact with its surroundings in a better manner. It helps the vehicle in perceiving, interpreting, and navigating the world in an accurate and effective manner.

Talk to our Solutions Expert

    * Mandatory fields

    We're committed to your privacy. Cogito uses the information you provide to us to contact you about our relevant content, products, and services. For more information, check out our Privacy Policy.