Proactive exploration
Delegate a question—or let the Agent dig first. It surfaces patterns before you know which chart to build.
Find the why—without waiting on an analyst
Ask a business question or hand the exploration to your Agent. On one semantic context, it cuts across dimensions, attributes change, and returns insights with evidence.
01 // Beyond dashboards
Exploration that finds the why
Traditional BI shows charts and waits for someone to dig. Bayeslab’s Agent explores first—so you move from “what changed” to “why it changed” without the analyst queue.
Cross-dimension cuts
Slice by segment, channel, product, region, and behavior—not one summary dashboard that hides the story.
From anomaly to root cause
Detect the spike or drop, then chase drivers until you have a verified explanation—not another screenshot.
02 // Agentic analysis
What the Agent actually does
Exploration is more than chat over a table. These are the analysis moves that turn a business question into a decision-ready answer.
Multi-dimensional exploration
Cut the data from statistical, segment, and behavioral angles—so you see drivers, not just a single rolled-up metric.
Automatic attribution
Break down why a metric moved: which channels, products, regions, or cohorts contributed—and which dragged results down.
Hypothesis testing
Propose and test explanations—e.g. pricing vs. fulfillment for a retention dip—and keep only what the evidence supports.
Anomaly & driver discovery
Spot outliers early, then drill into drivers instead of waiting for someone to notice a red cell on a dashboard.
Ask or delegate
Use natural language when you know the question—or let the Agent explore first when you only know something feels off.
Verified, reproducible insights
Conclusions come from executable analysis you can trace—not chat guesses that rewrite the numbers next session.
03 // Why trust the exploration
Built on semantics, not guesswork
Exploration only works if “Churn” means the same thing every time. Bayeslab reasons on your semantic model and remembers context across the analysis.
Semantic-powered exploration
The Agent explores using shared metrics and relationships—so LTV, Churn, and ARR stay consistent everywhere it digs.
Persistent analysis memory
Schema, metric definitions, and prior exploration stay in context. You don’t restart from a blank chat every session.
04 // In the field
From “retention dropped 14%” to a verified root cause
Challenge: “Identify why Month 3 retention dropped by 14% for our EMEA customer base in Q3.”
Result: The Agent isolated a localization mismatch in checkout flows and linked it to a legacy API update.
[DETECT] Anomaly in Month-3 retention for EMEA cohort.
[SLICE] Compare user_sessions vs payment_logs by locale.
[TEST] Regression check on checkout completion (95% CI).
[VALIDATE] Cross-check with external_api_status timeline.
[INSIGHT] Localization mismatch in checkout after API update.
In the Agentic BI workflow
05 // Get started
Explore with your AI analyst.
Connect your data, ask—or delegate—and turn exploration into insights, charts, live dashboards, and PPT reports.