Built-in LLM assistant and knowledge-base Q&A
Build enterprise AI apps by drag-and-drop, integrate multiple large models such as GPT / Tongyi / Doubao, with a built-in RAG knowledge base, prompt templates and AI workflows — so business users can build AI apps too.
Build enterprise AI apps in one place
From model integration to intelligent Q&A — build AI apps in one place
Visually chain model nodes, knowledge-base retrieval and tool calls to build complex logic with zero code.
Natively integrate 12+ large models including GPT / Tongyi / Wenxin / Doubao / DeepSeek / local Qwen.
One-click ingest of PDF / Word / Excel / web pages; vector retrieval + reranking keep answers precise and traceable.
Build a scenario-based prompt library with variables, versions and A/B testing to compare results.
Context memory with separated short- and long-term memory, plus streaming output and interrupt-and-resend.
Sensitive-word filtering, PII masking, content auditing and on-prem deployment keep enterprise data in-house.
High-frequency frontline AI scenarios, ready out of the box
Combining FAQ and product document libraries, automatically answers common questions 24/7 and escalates complex tickets to humans.
Turn internal policies, contracts and manuals into a knowledge base so employees and customers can simply "ask" for answers.
Automatically produce social posts, SMS and email copy based on brand assets and product selling points, doubling operational efficiency.
Ask business questions in natural language, auto-generate SQL and charts, so even the CEO can read operations at a glance.
A development assistant helps produce SQL, unit tests and API skeletons, improving consistency with an internal coding-standards library.
Clause extraction, risk-point identification and template comparison auto-generate review opinions, multiplying legal efficiency.
Build an enterprise AI app in four steps
Choose GPT / Tongyi / Doubao / local Qwen and other large models based on results, cost and compliance needs
Drag in PDF / Word / Excel / web pages, auto-chunk and vectorize to build a dedicated knowledge base
Pick a scenario-based prompt template or customize it, and configure tool calls, memory strategy and fallback
Publish to WeCom / DingTalk / Web / business systems with one click and auto-generate embed code
Enterprise-grade LLM engineering — secure, controllable and extensible
A unified interface adapts 12+ models including GPT-4o / Tongyi Qianwen / Wenxin / Doubao / DeepSeek / Claude
Built-in Milvus/PGVector vector store supporting hybrid retrieval + reranking + citation tracing
Prompts support version snapshots, A/B testing, rollback and result tracking, so iteration is data-driven
Native SSE streaming responses display conversations as they generate, with interrupt-and-resend rivaling ChatGPT
Track token usage and cost by app / user / department, with dual protection of alerts and quotas
Supports offline inference of local Qwen / Llama / ChatGLM so data never leaves the intranet, meeting finance and government compliance
Make AI truly land inside the enterprise
Drag-and-drop orchestration replaces hand-written LangChain, cutting projects from months to days.
Switch large models for the same app with one click, dynamically optimizing for results and cost.
Q&A logs automatically feed back into the knowledge base and prompts, so it understands the business better over time.
On-prem deployment + sensitive-word filtering + audit tracing remove compliance concerns when launching AI.
Questions you may care about
Natively supports public-cloud models such as OpenAI GPT-4o / GPT-4o-mini, Alibaba Tongyi Qianwen, Baidu Wenxin, ByteDance Doubao, DeepSeek, Claude and Gemini; it also supports open-source models such as local Qwen / Llama 3 / ChatGLM / Baichuan, all unified under OpenAI-style APIs.
Yes. We provide local inference-engine adapters (vLLM / Ollama / TGI) to deploy open-source models of 7B-72B parameters on the enterprise intranet; supports GPU cluster inference, quantization acceleration and multi-GPU parallelism, meeting high-compliance scenarios such as finance and government.
Supports PDF, Word, PPT, Excel, Markdown, TXT, HTML, web links, images (OCR extraction), audio (ASR transcription) and more; auto-chunks and vectorizes, supports table extraction and mixed text-image layouts, and a single base can hold tens of millions of document chunks.
The platform adds no markup and passes through the official billing of the chosen large model; it provides token-usage statistics, quota alerts and over-limit blocking by app / user / department to prevent abuse; for on-prem deployed models, billing is based on inference resource usage, which is more cost-effective in the long run.
Supports on-prem deployment so data never leaves the intranet; for public-cloud model scenarios it provides request masking, sensitive-word filtering and automatic PII redaction; all conversation logs can be stored encrypted + operation auditing + role-based access, meeting MLPS Level 3 and GDPR compliance.
Provides standard REST APIs, SSE streaming interfaces and webhook callbacks, embeddable into WeCom / DingTalk / business systems; supports OpenAPI tool calls so AI apps can directly invoke ERP / CRM APIs to query and modify orders — truly landing in the business.
1-on-1 dedicated consultant service to tailor an AI Apps solution for you