Enterprise Knowledge QA Agent

Inspiration

Sales and customer support teams in small and medium-sized businesses often rely on scattered documents: product sheets, pricing tables, FAQs, sales scripts, support policies, and internal notes. These files are frequently stored in different places and maintained by different people.

This creates a common problem: sales reps and support agents may answer customers with outdated, inconsistent, or unverifiable information. A normal AI chatbot can make this worse if it generates answers without understanding which source is trusted, who owns it, or when it was last updated.

Enterprise Knowledge QA Agent was created to solve this problem by using DataHub as a trusted context layer for AI agents.

What it does

Enterprise Knowledge QA Agent helps sales and customer support teams generate reliable customer responses based on trusted business knowledge.

The agent can:

• Load company knowledge assets such as FAQs, pricing sheets, product documents, support policies, and sales scripts.
• Use DataHub metadata to understand the source, owner, type, and update time of each knowledge asset.
• Answer customer questions with clear source citations.
• Detect outdated or conflicting knowledge, such as different prices appearing in different documents.
• Generate follow-up suggestions for sales and support teams.
• Help teams move from a generic chatbot to a trusted business agent that understands where the answer comes from.

How we built it

We designed the project around a simple workflow:

1. Prepare sample company knowledge assets, including product information, pricing, FAQ, support policy, and sales scripts.
2. Register or represent these assets through DataHub as trusted business context.
3. Let the AI agent retrieve relevant context before answering a customer question.
4. Generate a response with source references and metadata.
5. Detect conflicts between documents and surface them as knowledge quality issues.

The prototype is planned with Python and Streamlit for the demo interface, DataHub for the metadata and context layer, and an LLM API for answer generation.

Challenges we ran into

The biggest challenge is making the agent more than a simple RAG chatbot. A basic chatbot can retrieve text and generate an answer, but it does not know whether the source is current, who owns it, or whether another document contains conflicting information.

The key challenge is to make DataHub visible in the workflow: not only as a data catalog, but as the trust and context layer that helps the agent decide which knowledge source should be used.

Accomplishments that we're proud of

We are proud of designing a practical AI agent use case for real business teams.

Instead of building a generic assistant, this project focuses on a specific operational problem: helping sales and support teams answer customers with reliable, traceable, and up-to-date business knowledge.

The project also shows how metadata can improve AI agent reliability, especially in enterprise workflows where trust, ownership, and source quality matter.

What we learned

We learned that AI agents need more than powerful language models. For enterprise use cases, agents also need trusted context, source ownership, update history, and knowledge quality checks.

DataHub can play an important role as the context layer that connects business knowledge, metadata, and agent decision-making.

What's next for Enterprise Knowledge QA Agent

Next, we plan to:

• Connect the prototype more deeply with DataHub metadata and lineage.
• Add a knowledge conflict detection dashboard.
• Support more business knowledge formats, such as Google Docs, Notion exports, CSV files, and internal wiki pages.
• Add a review workflow so knowledge owners can approve or fix outdated information.
• Expand the use case from customer support to sales enablement, onboarding, and internal operations.

Built With

• ai
• csv
• datahub
• github
• large-language-models
• markdown
• metadata
• python
• rag
• streamlit