How OmniQuery Works
1. Upload Your Data
PDFs, Word docs, Excel files, CSV data, or connect databases
2. Ask in Plain English
Natural language questions about your data and insights
3. Get Smart Insights
AI agents analyze and combine data from all sources automatically
Multi-Agent Intelligence
Document Agent
Analyzes PDFs, Word docs for insights and patterns
SQL Agent
Processes structured data from Excel, CSV, databases
Hybrid Agent
Combines insights from multiple data sources
Error Handler
Provides alternative analysis when needed
Upload Your Data Sources
Unstructured Data
Examples: Financial reports, meeting notes, policy documents, research papers, customer feedback
Structured Data
Examples: Sales data, customer records, inventory, financial transactions, survey responses
Upload Steps
Select File Type
Choose "Documents" for PDFs/Word or "Structured Data" for Excel/CSV
Drag & Drop or Browse
Upload your files - multiple files supported
Add Metadata (Optional)
For structured data, add column descriptions for better analysis
Processing Complete
Files are automatically indexed and ready for queries
Ask Smart Questions
💬 Ask anything about your data:
Analytics Questions
• "What are the top 5 performing products this quarter?"
• "Show me revenue trends by region"
• "Identify outliers in our sales data"
• "What's our customer churn rate?"
Document Questions
• "What does the contract say about payment terms?"
• "Find key insights from customer feedback"
• "Summarize the Q4 financial report"
• "What are the main compliance requirements?"
Cross-Source Questions
• "Correlate sales data with customer satisfaction"
• "Compare budget vs actual spending from all sources"
• "How do market trends align with our performance?"
• "Find discrepancies between reports and data"
Insight Questions
• "What factors contribute to our best performance?"
• "Generate recommendations for improvement"
• "Predict next quarter's trends"
• "What should we focus on this month?"
Pro Tips for Better Results
- • Be specific about time periods: "last quarter", "this year", "since January"
- • Mention data sources when needed: "from Excel data", "according to reports"
- • Ask follow-up questions to dive deeper into insights
- • Use comparison words: "compare", "versus", "difference between"
Real Examples & Use Cases
Business Performance Analysis
Data Sources:
- • Sales_Q4_2024.xlsx
- • Financial_Report_Q4.pdf
- • Customer_Feedback.docx
- • CRM Database
Sample Queries:
- • "Why did revenue drop in October?"
- • "Which customers are most profitable?"
- • "Compare Q4 goals vs actual results"
Expected Result:
Cross-source analysis showing revenue trends, customer sentiment correlation, and actionable recommendations
HR & Employee Analytics
Data Sources:
- • Employee_Survey_2024.csv
- • HR_Policies.pdf
- • Performance_Reviews.xlsx
- • Exit_Interviews.docx
Sample Queries:
- • "What affects employee satisfaction most?"
- • "Find patterns in employee turnover"
- • "Compare policy changes with satisfaction scores"
Expected Result:
Insights on retention factors, policy effectiveness, and data-driven HR recommendations
Market Research & Strategy
Data Sources:
- • Market_Research_2024.pdf
- • Competitor_Analysis.xlsx
- • Social_Media_Data.csv
- • Industry_Report.docx
Sample Queries:
- • "How do we compare to competitors?"
- • "What market opportunities exist?"
- • "Analyze social sentiment vs market position"
Expected Result:
Strategic insights combining market data, competitive analysis, and growth opportunities
Quick Start in 3 Steps
Upload Files
Add your PDFs, Excel, Word docs
Ask Questions
Type natural language queries
Get Insights
Receive intelligent analysis