Shaip Generative AI Platform
Ensure your Generative AI is Responsible & Safe
LLM Development Lifecycle
Data Generation
High-quality, diverse, and ethical data for every stage of your development lifecycle: training, evaluation, fine-tuning, and testing.
Robust AI Data Platform
Shaip Data Platform is engineered for sourcing quality, diverse, and ethical data for training, fine-tuning, and evaluating AI models. It allows you to collect, transcribe, and annotate text, audio, images, and video for a variety of applications, including Generative AI, Conversational AI, Computer Vision, and Healthcare AI.With Shaip, you ensure that your AI models are built on a foundation of reliable and ethically sourced data, driving innovation and accuracy.
Experimentation
Experiment with various prompts and models, selecting the best based on evaluation metrics.
Evaluation
Evaluate your entire pipeline with a hybrid of automated and human assessment across expansive evaluation metrics for diverse use cases.
Observability
Observe your generative AI systems in real-time production, proactively detecting quality and safety issues while driving root-cause analysis.
Generative AI Use Cases
Question & Answering Pairs
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
Keyword Query Creation
Keyword query creation involves extracting the most relevant and significant words or phrases from a given text to form a concise query. This process helps in efficiently summarizing the core content and intent of the text, making it easier to search for or retrieve related information. The selected keywords are usually nouns, verbs, or important descriptors that capture the essence of the original text.
RAG Data Generation (Retrieval-Augmented Generation)
RAG combines the strengths of information retrieval and natural language generation to produce accurate and contextually relevant responses. In RAG, the model first retrieves relevant documents or passages from a large dataset based on a given query. These retrieved texts provide the necessary context. The model then uses this context to generate a coherent and accurate answer. This method ensures that the responses are both informative and grounded in reliable source material, improving the quality and accuracy of the generated content.
RAG Q/A Validation
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 Classification
Our experts can summarize the entire conversation or long dialogue by inputting concise and informative summaries of large volumes of text data.
Search Query Relevance
Search query relevance assesses how well a document or piece of content matches a given search query. This is crucial for search engines and information retrieval systems to ensure that users receive the most relevant and useful results for their queries.
| Search Query | Webpage | Relevance Score |
| Best hiking trails near Denver | Top 10 Hiking Trails in Boulder, Colorado | 3 – somewhat relevant ( since Boulder is near Denver but the page doesn’t mention Denver specifically) |
| Vegetarian restaurants in San Francisco | The Top 10 Vegan Restaurants in the San Francisco Bay Area | 4 – very relevant (because vegan restaurants are a type of vegetarian restaurant, and the list focuses specifically on the San Francisco Bay Area) |
Synthetic Dialogue Creation
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.
NL2Code
NL2Code (Natural Language to Code) involves generating programming code from natural language descriptions. This helps developers and non-developers alike to create code by simply describing what they want in plain language.
NL2SQL (SQL Generation)
NL2SQL (Natural Language to SQL) involves converting natural language queries into SQL queries. This allows users to interact with databases using plain language, making data retrieval more accessible to those who may not be familiar with SQL syntax.
Reasoning-Based Question
A reasoning-based question requires logical thinking and deduction to arrive at an answer. These questions often involve scenarios or problems that need to be analyzed and solved using reasoning skills.
Negative/Unsafe Question
A negative or unsafe question involves content that could be harmful, unethical, or inappropriate. Such questions should be handled with caution and typically require a response that discourages unsafe behavior or provides safe, ethical alternatives.
Multiple Choice Questions
Multiple choice questions are a type of assessment where a question is presented along with several possible answers. The respondent must select the correct answer from the provided options. This format is widely used in educational testing and surveys.
Why Choose Shaip?
End-to-End Solutions
Comprehensive coverage of all stages of Gen AI lifecycle, ensuring responsibility & safety from ethical data curation to experimentation, evaluation, & monitoring.
Hybrid Workflows
Scalable data generation, experimentation, & evaluation through a blend of automated and human processes, leveraging sme's to handle special edge cases.
Enterprise-Grade Platform
Robust testing and monitoring of AI applications, deployable in the cloud or on-premise. Seamlessly integrates with existing workflows.
