Generative AI Data Solutions

Generative AI Services: Mastering Data to Unlock Unseen Insights

Harness the power of generative AI to transform complex data into actionable intelligence.

Generative Ai

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The progress in Generative AI technologies is ceaseless, bolstered by fresh data sources, meticulously curated training and testing datasets, and model refinement via reinforcement learning from human feedback (RLHF) procedures.

Reinforcement Learning from Human Feedback (RLHF) in generative AI models leverages human insights, including domain-specific expertise, for behavioral optimization and accurate output generation. Fact-checking from domain experts ensures the model’s responses are not only contextually relevant but also trustworthy and reliable. Platforms like Shaip bridge this ecosystem by providing high-quality data labeling, Credential domain experts, domain-specific training, and evaluation services, enabling the seamless integration of human intelligence into the iterative fine-tuning of Large Language Models, thus fostering enhanced performance and safety in AI apps.

Generative AI Use Cases

1. Question & Answering

Question &Amp; Answering

Our experts can create Question-Answer pairs by thoroughly reading the entire document to enable companies to develop Gene AI. This can address queries by extracting the relevant info from a large corpus. Our experts create high-quality Q&A pairs such as:

» Generating Q&A for Contact Center Agent Support
» Creation of surface level (Direct data extraction from reference Text)
» Create deep level questions (Correlate with facts & insights not given in reference text)
» Developing Q&A based on Tabular Data

When creating Q&A datasets for generative AI models, it is important to focus on specific domains and types of documents relevant to the industry and contain the necessary information to answer common questions.

  • Product Manuals/ Product Documentation
  • Technical Documentation
  • Online forums & Reviews
  • Customer Service Data
  • Industry Regulatory Documents

2. Text Summarization

Our experts can summarize the entire conversation or long dialogue by inputting concise and informative summaries of large volumes of text data.

Text Summarization
Summarized Email Thread
Summarized Chat
Image Generation

3. Image Generation & Image Rendering

Train models with a large dataset of images with various features, such as objects, scenes, & textures, to generate realistic images, i.e., creating new product designs, marketing materials, or virtual worlds. We also offer 3D Content Creation, specializing in the intricate design of 3D characters with detailed geometry

Image Captioning

Transform how you interpret images with our advanced AI-powered Image Captioning service. We breathe life into images by generating precise and contextually rich descriptions, opening up new ways for your audience to interact and engage with your visual content more effectively.

Deepfake Detection Service

Identify & analyze manipulated digital media files, including images & videos. Our experts meticulously scan media content to detect subtle anomalies & inconsistencies that are indicative of deepfake manipulation. Our team verify the authenticity of the content, helping you to distinguish between genuine & artificially generated media.

4. Text Generation

Train models with a large dataset of text with various styles, such as news articles, fiction, and poetry, to generate text, such as news articles, blog posts, or social media content, to save time and money on content creation.

Text Generation


The main soundtrack of an arcade game. It is fast-paced and upbeat, with a catchy electric guitar riff. The music is repetitive and easy to remember, but with unexpected sounds, like cymbal crashes or drum rolls.

Generated audio


5. Audio Generation

Train models with a large dataset of audio recordings with various sounds, such as music, speech, and environmental sounds, to generate audio, such as music, podcasts, or audio books.

Speech Recognition

Speech Recognition

Train models that understand spoken language, i.e., applications, such as voice-activated assistants, dictation software, and real-time translation based on a large dataset of audio recordings of speech with corresponding transcripts.

Training Text-to-Speech Services

We offer a large dataset of audio recordings of human speech to train AI models to create natural, engaging voices for your applications, offering your users a unique and immersive auditory experience.

6. Machine Translation

Train models with a large multi-lingual dataset with corresponding transcription to translate text from one language to another, breaking down language barriers and making information more accessible.

7. Product Recommendations

Train models with a large dataset of customer purchase histories with labels indicating which products customers are most likely to purchase to offer accurate recommendations to customers to increase sales and improve customer satisfaction.

Product Recommendations

8. LLM Datasets Evaluation with Human Rating & QA Validation

In the world of machine learning, ensuring that a model understands and generates human-like text based on given prompts is paramount. This process involves rigorous dataset evaluation through human rating and quality assurance (QA) validation. Evaluators critically assess the prompt-response pairs in a dataset and rate the relevance and quality of the responses generated by a Language Learning Model (LLM).

9. LLM Datasets Comparison with Human Rating & QA Validation

Dataset comparison involves meticulous analysis of various response options for a single prompt. The objective is to rank these responses from best to worst based on their relevance, accuracy, and alignment with the context of the prompt.

Chatbot Training

10. Chatbot Training

Harness the power of gen AI to engage in meaningful interactions with users, answering queries, & providing solutions based on context. By leveraging techniques like Question & Answering and Text Summarization, chatbots can comprehend user intent, extract relevant information from vast databases, & provide concise response. 

Generative AI empowers chatbots in various domains, including customer support, product inquiries, troubleshooting, and even casual conversations. These bots can sift through product manuals, technical documentation, online forums, and more to provide the most accurate response to a user’s query.

Empowering Diagnoses with Generative AI:The Future of
Healthcare Intelligence

Elevate patient care and diagnosis by leveraging generative AI to sift through intricate health data.

Generative Ai Healthcare Ai

MedTech Solutions is at the forefront of offering expansive, varied datasets designed specifically to fuel generative AI applications in the healthcare sector. With a comprehensive grasp of the unique demands of medical AI, our mission is to supply data frameworks that promote precise, swift, and pioneering AI-driven diagnoses and treatments.

Healthcare Generative AI Use Cases

1. Question & Answering

Healthcare - Question &Amp; Answering

Our certified professionals meticulously review healthcare documents & literature to curate Question-Answer pairs, facilitating the development of Generative AI. This facilitates answering questions like suggesting diagnostic procedures, recommending treatments, & assisting doctors in diagnosing and providing insights on clinical case by filtering relevant information from extensive data banks. Our healthcare specialists produce top-tier Q&A sets like:

» Creating surface-level queries (Direct extraction from literature).
» Designing deep-level questions (Interlacing with insights and data not present in the primary source).
» Framing Q&A from Medical Tabular Data.

For robust Q&A repositories it’s imperative to center around:

  • Clinical Guidelines & Protocols 
  • Patient-provider interactions Data
  • Medical Research Papers 
  • Pharmaceutical Product Information
  • Healthcare Regulatory Documents
  • Patient Testimonials, Reviews, Forums & Communities

2. Text Summarization

Our healthcare specialists excel in distilling vast amounts of information into clear & concise summaries i.e., doctor-patient conversation, EHR, or research articles, we ensure that professionals can quickly grasp core insights without having to sift through the entirety of the content.Our offerings include:

  • Text-based EHR Summarization: Efficiently encapsulate patient medical histories, treatments, and other vital data into an easily digestible format.
  • Doctor-Patient Conversation Summarization: Extract and present the key points from medical consultations, ensuring that no critical detail is overlooked.
  • PDF-based Research Article: Distill complex medical research papers into their fundamental findings, allowing for faster & effective comprehension.
  • Medical Imaging Report Summarization: Convert intricate radiology or imaging reports into simplified summaries that highlight main findings.
  • Clinical Trial Data Summarization: Break down extensive clinical trial results into their most crucial takeaways, aiding in swift decision-making.

3. Synthetic Data Creation

Synthetic data is critical, especially in the healthcare domain, for various purposes such as AI model training, software testing, and more, without compromising patient privacy. Here’s a breakdown of the listed synthetic data creations:

3.1 Synthetic Data HPI & Progress Notes Creation

This involves the generation of artificial, but realistic, patient data that mimics the format and content of a patient’s history of present illness (HPI) and progress notes. This synthetic data is valuable for training machine learning algorithms, testing healthcare software, and conducting research without risking patient privacy.

3.2 Synthetic Data EHR Note Creation

This process entails the creation of simulated Electronic Health Record (EHR) notes that are structurally and contextually similar to real EHR notes. These synthetic notes can be used for training healthcare professionals, validating EHR systems, and developing AI algorithms for tasks such as predictive modeling or natural language processing, all while maintaining patient confidentiality.

Synthetic Data Ehr Note Creation

3.3 Synthetic Doctor-Patient Conversation Summarization in Various Domains

This involves generating summarized versions of simulated doctor-patient interactions across different medical specialties, such as cardiology or dermatology. These summaries, although based on fictional scenarios, resemble real conversation summaries and can be used for medical education, AI training, and software testing without exposing actual patient conversations or compromising privacy.

Synthetic Doctor-Patient Conversation

Core Features


Comprehensive AI Data

Our vast collection spans various  categories, offering an extensive selection for your unique model training.

Quality Assured

We follow stringent quality assurance procedures to ensure data accuracy, validity, and relevance.

Diverse Use Cases

From text and image generation to music synthesis, our data sets cater to various generative AI applications.

Custom Data Solutions

Our bespoke data solutions cater to your unique needs by building a tailored dataset to meet your specific requirements.

Security and Compliance

We adhere to the data security & privacy standards. We comply with GDPR & HIPPA regulations, ensuring user privacy.


Improve accuracy of generative AI models

Save time & money on data collection

Accelerate your time
to market

Gain a competitive

Build Excellence in your Generative AI with quality datasets from Shaip

Generative AI refers to a subset of artificial intelligence focused on creating new content, often resembling or imitating given data.

Generative AI operates through algorithms like Generative Adversarial Networks (GANs), where two neural networks (a generator and a discriminator) compete and collaborate to produce synthetic data resembling the original.

Examples include creating art, music, and realistic images, generating human-like text, designing 3D objects, and simulating voice or video content.

Generative AI models can utilize various data types, including images, text, audio, video, and numerical data.

Training data provides the foundation for generative AI. The model learns the patterns, structures, and nuances from this data to produce new, similar content.

Ensuring accuracy involves using diverse and high-quality training data, refining model architectures, continuous validation against real-world data, and leveraging expert feedback.

The quality is influenced by the volume and diversity of training data, the complexity of the model, computational resources, and the fine-tuning of model parameters.