End-to-end Solutions for LLM Development Lifecycle

Data Generation

High-quality, diverse, and ethical data for every stage of your development lifecycle: training, evaluation, fine-tuning, and testing.

Field Data Collection

Gather domain-specific, real-world, or synthetic data from users globally using the Shaip Data Platform for training and fine-tuning.

Bring Your Own Data

Integrate data from production inferences via API, Python SDK, or by uploading JSON files to evaluate and fine-tune your Gen AI models.

RLHF Data

Utilize feedback from subject matter experts, who review model responses and generate RLHF feedback for fine-tuning.

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.

Prompt Management

Explore the platform featuring dozens of models to experiment with multiple prompts side-by-side, and save your prompts with version history.

Model Comparison

Compare responses from different prompts and models to select the best model for your use case based on evaluation metrics and human feedback.

Model Catalog

Choose from a wide range of models including OpenAI, Google, Azure, Anthropic, Cohere, or open-source models from Hugging Face, Meta, Mistral, or your custom models.

Evaluation

Evaluate your entire pipeline with a hybrid of automated and human assessment across expansive evaluation metrics for diverse use cases.

50+ Auto-evaluator Metrics

Utilize 50+ metrics to evaluate aspects such as hallucination, correctness, relevance, groundedness, faithfulness, and toxicity.

Custom & Open-Source Evaluators

Integrate custom evaluations and open-source tools like Ragas and Guardrails.

Offline & Online Evaluation

Conduct evaluations on any dataset via Python SDK, on the platform, or run regression tests in a CI/CD pipeline or on production traces.

Human Evaluation

Employ human annotators with subject matter expertise to assess metrics like performance, reliability, and safety.

Observability

Observe your generative AI systems in real-time production, proactively detecting quality and safety issues while driving root-cause analysis.

Evaluate Entire RAG Pipeline

Gain a comprehensive view of every trace in granular detail, including retrieval context and generated responses, with selected evaluators.

Real-time Monitoring

Continuously evaluate your Gen AI system’s quality and safety with guardrail metrics, identifying and debugging issues, and conducting root cause analysis.

Analytics Dashboard

Track historical records of your Gen AI pipeline’s performance, cost, usage, and evaluation metrics over time, by model, environment, topic, and more.