Harness the power of generative AI to transform complex data into actionable intelligence.
Empowering teams to build world-leading AI products.
The progress of Generative AI technologies is continuous, driven by new data sources, meticulously curated training and testing datasets, and model refinement through reinforcement learning from human feedback (RLHF)
RLHF in generative AI 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. Shaip provides accurate data labeling, credential domain experts, and evaluation services, enabling seamless integration of human intelligence into the iterative fine-tuning of Large Language Models.
Medical Imaging Analysis: Generate and enhance medical images for diagnostics.
Clinical Documentation: Automate medical record summarization and transcription.
Fraud Detection: Generate scenarios to test fraud detection systems.
Risk Assessment: Analyze and simulate financial risks with AI models.
Autonomous Driving: Simulate road scenarios for training self-driving models.
Voice Command Systems: Enhance voice recognition and response accuracy for in-car systems.
Product Recommendations: Generate personalized recommendations using user behavior.
Visual Content Creation: Create product images, videos, and descriptions.
Claim Processing: Automate claim summarization and fraud detection.
Risk Modeling: Simulate scenarios to evaluate and predict risks.
Chatbots: Enhance customer service with AI-powered virtual assistants.
Content Recommendations: Suggest personalized content for users based on their preferences.
We gather and curate data to refine language models for precision and accuracy.
We craft and optimize natural language prompts to mirror diverse user interactions with your AI.
Our service creates specialized text for sectors like legal and medical to train your domain-focused AI.
Our extensive network enables a thorough comparison of AI answers to enhance model accuracy and dependability.
Our approach uses flexible scales to measure and reduce toxic content in AI-generated communications accurately.
Our tailored feedback ensures that AI responses have the appropriate tone & brevity for specific user scenarios.
We assess gen AI results for quality across markets and languages to fine-tune AI to align with market-specific needs through RLHF.
We rigorously evaluate AI-generated content to ensure it is factual and realistic to prevent the spread of misinformation.
Create Question-Answer pairs by thoroughly reading large documents (Product Manuals, Technical Docs, Online forums & Reviews, Industry Regulatory Documents) to enable companies to develop Gen AI by extracting the relevant info from a large corpus. Our experts create high-quality Q&A pairs such as:
» Q&A pairs with multiple answers
» Creation of surface level questions (Direct data extraction from reference Text)
» Create deep level questions (Correlate with facts & insights not given in reference text)
» Query Creation from Tables
Our experts can summarize the entire conversation or long dialogue by inputting concise and informative summaries of large volumes of text data.
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.
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.
Caption
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
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.
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.
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).
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.
Synthetic Dialogue Creation harnesses the power of Generative AI to revolutionize chatbot interactions and call center conversations. By leveraging AI’s capacity to delve into extensive resources such as product manuals, technical documentation, and online discussions, chatbots are equipped to offer precise and relevant responses across a myriad of scenarios. This technology is transforming customer support by providing comprehensive assistance for product inquiries, troubleshooting issues, and engaging in natural, casual dialogues with users, thereby enhancing the overall customer experience.
In Generative AI, Image Summarization, Rating & Validation involve machine learning models that curate and assess images, generating summaries and quality ratings. Human feedback fine-tunes AI accuracy, ensuring the content meets nuanced standards, enhancing reliability.
Fast-track your transformation with our rapid Proof of Concept (POC) deployments—turning ideas into reality within weeks.
AI isn’t one-size-fits-all. We create industry-specific prompts to ensure precise, relevant, and insightful AI-generated content for your audience.
We ensures GDPR, HIPAA, and SOC 2 compliance, protecting sensitive AI training data.
We provide industry-focused datasets for healthcare, legal, fintech, and other specialized fields.
We deliver unmatched expertise in cloud, data, AI, and automation through our technology partner ecosystem.
We deliver clean, structured, and bias-free datasets that improve the performance of RAG-powered AI applications.
Ever scratched your head, amazed at how Google or Alexa seemed to ‘get’ you? Or have you found yourself reading a computer-generated essay that sounds eerily human? You’re not alone.
Human intelligence to transform Natural Language Processing (NLP) into high-quality training data for machine learning with text and audio annotation.
AI feeds on copious amounts of data & leverages machine learning (ML), deep learning (DL) & natural language processing (NLP) to continually learn & evolve.
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.
