Ask Complex Questions, Get Real Answers
"Which marketing channels drive the highest customer lifetime value for our enterprise segment?"
✓ Explored 12 relevant tables across marketing, sales, and finance schemas
✓ Joined customer data with transaction history and attribution
✓ Calculated LTV by cohort and channel
Result: Webinars drive 3.2x higher LTV ($47K vs $14K avg) for enterprise segment
"What's causing the spike in churn for users who signed up in Q3 2024?"
✓ Analyzed Q3 2024 cohort behavior patterns
✓ Compared feature adoption vs. other cohorts
✓ Identified correlation with onboarding flow change
Result: Users who skipped tutorial (65% of Q3) churned 4x faster. Tutorial was made optional on Sept 1.
"How do conversion rates differ between mobile and desktop users across different product categories?"
✓ Segmented sessions by device type and product category
✓ Calculated conversion funnels for each segment
✓ Identified statistically significant differences
Result: Mobile converts 2.3x better for impulse categories (under $50), desktop dominates for research-heavy purchases (electronics, furniture)
"Are there patterns in feature usage that predict upgrade from free to paid tiers?"
✓ Analyzed feature usage for upgraded vs. non-upgraded users
✓ Built correlation matrix across 47 features
✓ Identified top predictive behaviors
Result: Users who export data 3+ times in first month convert at 68% (vs 12% baseline). Export feature is strong intent signal.
"What's the average time between first contact and closed deal for deals over $100K?"
✓ Joined CRM data with deal history
✓ Calculated time deltas for deals > $100K
✓ Segmented by industry and deal source
Result: Avg 127 days. Referrals close in 89 days, cold outbound takes 156 days. Enterprise (500+ employees) averages 184 days regardless of source.
How It Works
Ask Your Question in Natural Language
No SQL required. Just describe what you want to know in plain English. The AI understands business terminology and complex analytical concepts.
Agent Explores Your Warehouse
The AI autonomously searches through your data warehouse, identifying relevant tables, understanding relationships, and mapping your data schema to answer your specific question.
Corrects Data Issues on the Fly
Encounters duplicates, nulls, or schema inconsistencies? The agent handles them automatically with appropriate data cleaning techniques, documenting every decision it makes.
Synthesizes Insights with Supporting Queries
Get a clear answer to your question along with all the SQL queries used, intermediate results, and statistical context. Everything is traceable and verifiable.
You Verify Every Step
Review the agent's work, dig into the queries, check assumptions. You're in control—the AI is transparent about its reasoning and methods.
Why You Can Trust the Analysis
Every Query Is Visible
See exactly what SQL was run, what tables were accessed, and how data was transformed. No black box.
Assumptions Are Documented
When the agent makes decisions about handling nulls, duplicates, or ambiguous data, it tells you exactly what it did and why.
Statistical Context Included
Get sample sizes, confidence levels, and data quality metrics alongside your insights so you know how reliable the analysis is.
Caveats Are Highlighted
The AI flags potential issues like small sample sizes, missing data, or limitations in the analysis so you can interpret results appropriately.
Faster Insights = Better Decisions
Traditional Approach
- • Submit ticket to data team
- • Wait 3-5 days for someone to pick it up
- • Back-and-forth on requirements (2-3 days)
- • Wait for analysis (2-4 days)
- • Review results, request changes (1-2 days)
With AI Agents
- • Ask your question
- • Get comprehensive answer
- • Verify and refine if needed