AI Assistant Development for Corporate Knowledge Base
Corporate knowledge base — Confluence, Notion, SharePoint, internal wikis — contains vast amounts of information employees cannot find or don't know exists. AI assistant makes this knowledge accessible through dialogue: employee asks natural language question and receives answer with sources.
RAG Assistant Architecture
Uses Chroma for vector storage, OpenAI embeddings, Claude for reasoning, and conversation history management.
from anthropic import Anthropic
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
class CorporateKBAssistant:
def __init__(self):
self.vectorstore = Chroma(collection_name="corporate_kb")
self.conversation_history = {}
def answer(self, question: str, user_id: str) -> dict:
# Search knowledge base
results = self.vectorstore.similarity_search(question, k=5)
# Build context from top results
context = "\n\n".join([doc.page_content for doc in results])
# Call Claude with grounded context
response = client.messages.create(
model="claude-sonnet-4-5",
messages=[{
"role": "user",
"content": f"Question: {question}\n\nContext: {context}"
}],
system="Answer only based on provided context. Cite sources."
)
return {
"answer": response.content[0].text,
"sources": [doc.metadata["title"] for doc in results[:3]]
}
Confluence Integration
- OAuth authentication
- Incremental indexing of pages
- Automatic sync on page updates
- Support for labels and space filtering
Practical Case: IT Company (200 Employees)
Problem: 45 minutes/day spent searching for information. Confluence with 3000+ pages, employees didn't know where to look.
Solution:
- Indexed 3200 Confluence pages
- Telegram + Slack bots for access
- Daily auto-sync of new pages
Results:
- Information search time: 45 min → 8 min per day
- "Who knows where this?" requests: -71%
- Answer accuracy: 4.3/5.0
- 9% of questions directed to relevant team members
Timeline
- RAG setup + Confluence sync: 1 week
- Telegram/Slack integration: 3–5 days
- Auto-sync + incremental indexing: 1 week
- Multi-source support (Notion + Jira): +1 week each