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What Product Categories do High Spenders like to shop?
"High spenders" matches two Segments. Pick one — or rephrase.
✓ "Spend > $5,000 / year" validated and added to your Context Graph
Six Moments · One Walkthrough
Each moment below is a real question from a real evaluation run. Each shows one thing Spotonix does that an LLM-only BI tool structurally can't.
01 / Learns Your Business
Schema, your modeling layer, BI assets, every SQL query your team has ever run. Spotonix studies the corpus and constructs a Context Graph — yours alone.
Your Context Graph · TPC-DS retail
02 / Clarify once · Memory forever
First time Spotonix sees an ambiguous term, it asks you to clarify or rephrase. Every subsequent question uses your validated definition automatically — no re-asking. The clarification is a one-time tax. The memory is permanent.
"High spenders" matches two Segments. Pick one — or rephrase.
Plan · auto-applied from Context Graph
Clarification once. Memory forever. Every disambiguation strengthens the Context Graph — and shortens the next question.
03 / Interpret
A real, multi-concept question. Every interpretation is a graph — named nodes, named edges — that you can read in ten seconds. The plan is the trust mechanism. SQL only compiles after the algebra closes.
04 / Semantically correct
“Sales” living in three tables under three names is the easy part. The hard part: sales isn’t revenue, gross isn’t net, and a unit price was never meant to be summed. An LLM-only tool conflates them and answers — confidently. Spotonix binds every metric to its real definition — family, variant, gross-vs-net, channel, additivity — and when the binding would be wrong or ambiguous, it refuses or asks instead of guessing.
05 / Discovery
Sometimes the right question is "show me what's interesting." Spotonix proposes four candidate Segmentations — each with its own BI Algebra — and lets you drill into any of them.
Three customer-related dimensions surfaced from your Context Graph — all joined to store_sales, catalog_sales, web_sales. Each attribute below is an axis you can segment on; no values fetched yet.
Dimension
customeridentity & lifecycle
c_preferred_cust_flag 2 values flag_partition c_birth_year date cohort_window c_first_sales_date_sk date cohort_window Dimension
customer_demographicsdemographic profile
cd_gender 2 values cross_segment cd_marital_status 5 values cross_segment cd_education_status 6 values cross_segment cd_credit_rating 4 values cross_segment Dimension
customer_addressgeographic · hierarchical
ca_state 50+ values dimension_drilldown ca_city many dimension_drilldown ca_zip many dimension_drilldown 06 / Compound
Watch five real questions play through. The Segments and Calculations from each Answer persist — and the reuse rate climbs as the graph fills in. The 100th question is dramatically faster than the 1st because most of its building blocks already exist.
Three Ways to Experience It
Start with the hosted demo, then bring your own artifacts into a guided evaluation.
Fastest · 60 seconds
A working Spotonix instance against a sample retail dataset. Verify your work email, then sign in with Google.
Request access →Most Relevant · 5 minutes
See how Spotonix interprets your own DAX models — dry-run, no warehouse credentials required.
Coming soon · request early access →Deepest · founder-led
Request access first, then use your questions, data challenges, or PBIX files in a focused follow-up.
Request access →