AI Internal Knowledge Search - Company Brain Platform
Manual and Automation QA Engineer
RAG-powered enterprise search platform that provides context-aware answers by searching across documents, emails, and Notion workspaces.
Tools & Technologies
Testing Tools
Technologies
Problem Statement
Enterprises struggled with knowledge silos where critical information was scattered across documents, emails, Notion pages, and various repositories. Employees spent hours searching for answers, leading to reduced productivity and inconsistent information retrieval.
Approach
Designed and executed comprehensive test suites for RAG pipeline accuracy, document ingestion workflows, semantic search relevance, and multi-source data synchronization. Validated context retrieval, answer generation quality, and source citation accuracy.
Testing & Automation Strategy
Collaborated with AI/ML engineers to test vector embedding quality, retrieval precision, and LLM response accuracy. Performed integration testing for Google Workspace, Outlook, Notion, and Confluence connectors. Conducted load testing to ensure scalability across large document corpuses.
CI/CD Integration
Integrated automated API tests with Jenkins for continuous validation of search relevance, document indexing accuracy, and RAG pipeline performance. Set up monitoring for query latency, retrieval accuracy, and hallucination detection.
Before vs After Comparisons
Information Retrieval Speed
Employees manually searching through multiple platforms - Slack, email, Notion, Google Drive - often asking colleagues when search fails.
Single AI-powered search interface querying all connected sources with context-aware answers and source citations.
Key Improvements
Avg Search Time
99%Sources Checked
80%Search Success Rate
67%Colleague Interrupts
87%Answer Accuracy & Context
Traditional keyword-based search returning document lists without understanding context or providing direct answers.
RAG pipeline retrieves relevant chunks, LLM synthesizes context-aware answers with automatic source citations.
Key Improvements
Answer Accuracy
104%Context Understanding
Source Citation
400%Follow-up Needed
79%Multi-Source Data Integration
Information trapped in separate systems with no cross-platform search, requiring manual navigation between tools.
Connected integrations with Notion, Google Docs, Outlook, Confluence with real-time sync and unified vector index.
Key Improvements
Connected Sources
Data Freshness
400%Cross-ref Capability
Onboarding Impact
86%Enterprise Search Scalability
Native platform search with limited results, no ranking intelligence, and performance degradation at scale.
Vector database with semantic embeddings, distributed architecture, and intelligent relevance ranking.
Key Improvements
Document Capacity
4900%Query Latency
73%Concurrent Users
1900%Relevance Ranking
138%Knowledge Management ROI
Employees spending significant time searching, re-creating existing content, and waiting for answers from colleagues.
Instant answers from company knowledge base, reduced duplication, and preserved institutional knowledge.
Key Improvements
Time Lost/Employee/Week
90%Duplicate Content Created
86%Knowledge Retention
138%Annual Cost (100 emp)
90%Information Retrieval Speed - Key Improvements
Answer Accuracy & Context - Key Improvements
Multi-Source Data Integration - Key Improvements
Enterprise Search Scalability - Key Improvements
Knowledge Management ROI - Key Improvements
Code Examples
RAG Search API Test
Automated test for validating RAG-powered semantic search and context retrieval.
@pytest.mark.asyncio
async def test_rag_search_accuracy():
"""Test RAG pipeline returns accurate, sourced answers."""
query = "What is our company's remote work policy?"
response = await client.post(
"/api/v1/knowledge/search",
json={"query": query, "sources": ["notion", "docs", "email"]},
headers={"Authorization": f"Bearer {API_TOKEN}"}
)
assert response.status_code == 200
result = response.json()
# Validate answer structure
assert "answer" in result
assert "sources" in result
assert len(result["sources"]) > 0
# Validate source citations
for source in result["sources"]:
assert "document_id" in source
assert "title" in source
assert "relevance_score" in source
assert source["relevance_score"] >= 0.7
# Validate response time
assert response.elapsed.total_seconds() < 2.0Document Ingestion Test
Test for validating multi-source document indexing and vector embedding.
@pytest.mark.asyncio
async def test_document_ingestion_pipeline():
"""Test document ingestion from multiple sources."""
# Trigger sync for Notion workspace
sync_response = await client.post(
"/api/v1/connectors/notion/sync",
json={"workspace_id": TEST_WORKSPACE_ID},
headers={"Authorization": f"Bearer {API_TOKEN}"}
)
assert sync_response.status_code == 202
job_id = sync_response.json()["job_id"]
# Poll for completion
status = await wait_for_job_completion(job_id, timeout=300)
assert status["state"] == "completed"
# Verify documents indexed
stats = await client.get(f"/api/v1/index/stats")
assert stats.json()["total_documents"] > 0
assert stats.json()["vector_count"] > 0
# Verify search works on new documents
search_result = await client.post(
"/api/v1/knowledge/search",
json={"query": "newly indexed content test"}
)
assert search_result.status_code == 200Results & Impact
Achieved 92% answer accuracy with proper source citations. Reduced average information retrieval time from 45 minutes to under 30 seconds. Successfully indexed 500K+ documents across multiple data sources with 99.5% sync accuracy. Platform maintained sub-2-second query response times under concurrent user load.
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