TianRangXiaoBai

Overview

Tianrang Xiaobai (also referred to as Xiaobai LLM APP Stack) is an enterprise-grade, all-in-one full-stack development platform designed for building large language model (LLM) applications at scale. With the release of version 2.8, the platform reinforces the vision that LLM applications go far beyond simple “chat” use cases, providing a unified environment for teams to move from experimentation to production deployment. It integrates prompt engineering, retrieval-augmented generation (RAG), AI agents, model operations, and API integration into a single workflow, enabling organizations to accelerate the entire lifecycle of AI application development.

At the core of the platform is a prompt integrated development environment (IDE) that allows developers to create, edit, and test prompts for large language models with ease. The built-in RAG capabilities enable applications to retrieve relevant information from large volumes of data, thereby enhancing the accuracy and context-awareness of model outputs. For more complex interaction patterns, the AI Agent feature personalizes user query processing flows, supporting multi-step or context-aware conversations and workflow orchestration. Additionally, large model operations management covers deployment, monitoring, and maintenance tasks, ensuring that models remain stable and performant in production. The API integration layer lets teams embed LLM functions into existing enterprise applications and services, while the application marketplace offers a rich collection of templates and pre-built applications to spark innovation and accelerate time-to-market.

Model flexibility is a key advantage. The platform openly integrates with multiple mainstream large models, including ERNIE Bot by Baidu, Baichuan, iFLYTEK SPARK, Meta Llama 2, Tianrang Xiaobai’s proprietary model, Hunyuan by Tencent, MiniMax, PanGu by Huawei, and Qwen by Alibaba. Developers are supported by a comprehensive multi-model evaluation system that enables systematic comparisons of model performance across different tasks and metrics. This not only helps in selecting the most suitable model for specific business needs but also reduces vendor lock-in and provides the flexibility to adapt as the LLM landscape evolves.

For enterprise customers, Tianrang Xiaobai offers flexible deployment and consumption models. Access can be provisioned through API calls with usage-based billing, or through private deployment and customized service options for organizations with strict security, privacy, and compliance requirements. The platform is backed by multiple industry recognitions and certifications, including information security management system certification, capability maturity model integration (CMMI) certification, and recognition as a high-tech enterprise. These credentials, together with its robust architecture, make Tianrang Xiaobai a dependable choice for organizations seeking to industrialize LLM applications while maintaining control over security and operational reliability.

Core capabilities

Prompt Integrated Development Environment

Create, edit, and test prompts for LLMs in a dedicated IDE.

Retrieval-Augmented Generation (RAG)

Retrieve relevant information from large datasets to enhance model answer quality.

AI Agent Orchestration

Personalize user query processing flows and build multi-step, context-aware conversational workflows.

Large Model Operations Management

Deploy, monitor, and maintain LLMs to ensure production stability.

API Integration

Embed LLM capabilities into existing applications and services via well-defined APIs.

Application Marketplace

Access a variety of pre-built LLM applications and templates to accelerate innovation.

Multi-Model Support

Connect to mainstream models such as Baidu ERNIE, Baichuan, iFLYTEK SPARK, Meta Llama 2, Tencent Hunyuan, MiniMax, Huawei PanGu, Alibaba Qwen, and Xiaobai.

Multi-Model Evaluation System

Compare model performance across tasks to select the most suitable model.

Use cases

Building enterprise knowledge base Q&A assistants

Developing intelligent customer service chatbots

Generating content and documents with LLMs

Creating RAG-based applications for domain-specific information retrieval

Automating workflow processes with AI agents

Selecting and benchmarking LLM models for production

Deploying private and secure LLM applications for regulated industries