Phase 1
Knowledge foundation
Build ingestion, metadata, hybrid retrieval, reranking, permissions and source-aware QA.
项目 · 智能体与生成式 AI
工业智能体 AI 平台
A multilingual industrial AI platform combining source-grounded knowledge retrieval with agentic workflows, tool use, multimodal interaction and evaluated LLM responses.
项目概览
Industrial organisations often store valuable knowledge across manuals, policies, reports, specifications, databases and project systems.
The platform combines modern RAG with hybrid retrieval, reranking and citations, then adds agent orchestration so specialised workflows can use tools, call APIs and preserve useful state across multi-step work.
The architecture is designed for responsible deployment with access controls, structured outputs, human approval where needed, tracing, feedback and evaluation.
技术与架构
Next.js and TypeScript user experience
FastAPI or Node.js AI and tool services
PostgreSQL with pgvector or equivalent vector storage
Hybrid retrieval, metadata filters and reranking
LLM and multimodal model routing
Agent orchestration, tool calling and MCP-ready integrations
Specialist agent collaboration patterns
Tracing, evaluation, guardrails and source citation
潜在价值
Faster access to approved technical knowledge
Automation of multi-step knowledge workflows
More transparent answers with citations and evaluation
Reusable platform for internal and customer-facing AI
A controlled path from assistants to production agents
路线图
分阶段验证路径。
Phase 2
Agentic workflows
Add specialised agents, tool and API use, memory, multimodal inputs and approval points.
Phase 3
Production evaluation
Add traces, automated and human evaluation, guardrails, monitoring, cost controls and deployment governance.