NEEAI

Platform Support

We support multiple open-source LLM platforms and tools to help enterprises quickly deploy AI applications and achieve intelligent transformation

Agent PlatformsLLM Service EnginesFlexible DeploymentContact Us

Agent Platforms

We support mainstream agent development platforms to help you quickly build and deploy AI applications

BISHENG

BISHENG

Development platform for building complex applications based on open-source models and knowledge bases

Coze

Coze

Next-generation one-stop AI Bot development platform

Dify

Dify

LLM application development platform integrating AI workflows, RAG retrieval and other capabilities

FastGPT

FastGPT

Rapidly build AI applications based on large language models

MaxKB

MaxKB

Knowledge base Q&A system based on LLM, ready to use out of the box

LLM Service Engines

High-performance inference engines providing powerful underlying support for your AI applications

Ollama

Ollama

Tool to easily run large language models locally

vLLM

vLLM

Fast and easy-to-use LLM inference service library

Xinference

Xinference

Powerful and feature-rich distributed inference platform

L

Llama.cpp

Run LLaMA models with C++, supporting consumer-grade hardware

S

ScaleLLM

High-performance and cost-effective LLM inference engine

Comprehensive Compatibility

Supports multiple open-source LLM platforms and tools, seamlessly integrating with existing systems

  • Supports mainstream open-source LLM platforms such as BISHENG, Dify, FastGPT, etc.
  • Compatible with multiple inference engines including vLLM, Ollama, Xinference, etc.
  • Can seamlessly integrate with existing enterprise systems such as OA, CRM, ERP, etc.
  • Provides standardized APIs for quick integration with various business systems

High-Performance Inference

Optimized inference engines to improve model response speed and concurrent processing capabilities

  • Optimized based on high-performance inference engines such as vLLM and Xinference
  • Supports model quantization and distillation technologies to reduce resource consumption
  • Provides dynamic batching and continuous batching functions
  • Optimizes CUDA and CPU operations to improve inference efficiency

Flexible Deployment

Supports cloud, edge, and on-premises deployment to meet different scenario requirements

  • Supports deployment in various cloud environments including public cloud, private cloud, and hybrid cloud
  • Provides edge computing deployment solutions to reduce latency and improve response speed
  • Supports on-premises deployment to meet data security and compliance requirements
  • Containerized deployment support for Docker and Kubernetes

Need support for other platforms?