AI Workflow Lab Demo

AI Business Process Knowledge Assistant

Ask questions about approved vendor onboarding process documents and get grounded answers with source references.

This demo shows how embeddings and vector search can retrieve relevant process sections before an LLM writes a grounded answer.

Try Demo How RAG works here

Static demo using synthetic process documents. No real business documents are uploaded or queried.

Document set

Approved Process Documents

Sample questions

Ask a process question

Retrieval

Question grounding progress

Why this matters

Manual Search vs RAG-Assisted Process Knowledge

Manual process lookup

Employee searches PDFs or asks colleagues
Answers may depend on memory
Missing policy details can be overlooked
Unsupported questions may get guessed

RAG-assisted process knowledge

Assistant retrieves relevant approved sections
Answer is grounded in source documents
Sources and limitations are shown
Assistant says when answer is not found

How it works

How RAG Works in This Demo

Process documents are converted into chunks.
Each chunk is converted into an embedding.
The user question is embedded and matched against the vector database.
The LLM answers using only the retrieved chunks and source references.

The public page uses precomputed static demo answers. The backend implementation is designed to support real embedding-based retrieval and grounded LLM answers from indexed process documents.

Responsible AI

Responsible AI Design

The assistant answers from approved process documents, not generic memory.
Source references are shown with each answer.
Unsupported questions return a no-answer response.
Demo documents are synthetic and safe for public display.
Public demo does not upload or process real business documents.